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The International Journal of Robotics Research

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Description
Aims and Scope
Editorial Board
Abstracting / Indexing
Submission Guidelines

A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research.

IJRR offers incisive and thought-provoking original research papers and articles, perceptive reviews, and lively editorials on ground-breaking trends issues, technical developments, and theories in robotics by the outstanding scholars and practitioners in the field. The Journal covers more than just narrow technical advances-it embraces a wide variety of topics. IJRR only publishes work of archival value, which is produced to advance science and technology in this field, and stays valuable in time. To do so the work must be original, solid, and useful for others to build upon.

Consistently ranked in the top 3 in its category of the Thomson Scientific JCR, IJRR publishes scholarly articles that provides engineers, researchers, and scientists with the very best of current research on robotics research - from applied mathematics to artificial intelligence to computer science, to psychological, cognitive and behavioural sciences, to electrical and mechanical engineering.

IJRR exclusively operates on the basis of peer reviews, with no professional editor external to the research community judging on scientific matters. All submitted manuscripts are reviewed by at least two expert reviewers of appropriate standing in the field of robotics research, in a single-blind scheme (reviewer names are concealed from the submitting authors).

There is no page limit for IJRR submissions. The rule is however that a paper should be as long as necessary, and no longer: conciseness is highly valued.

IJRR also publishes high quality, peer reviewed datasets, accompanied by adequate text material to illustrate them and their usage in the form of a regular manuscript.

Multimedia (mostly video or data) extensions are most welcome parts of an IJRR paper, as they concur to illustrate and demonstrate its results.

This journal is a member of the Committee on Publication Ethics (COPE).

All issues of IJRR are available to browse online.

It is the policy of The International Journal of Robotics Research to encourage the application of theoretical advances to real problems and data in Robotics, intended here in its broadest meaning, as per Sir M. Brady’s definition: “the intelligent link between perception and action”. Results should represent a significant rather than incremental advance, and should be verified appropriately according to the topic. Experimental results are strongly encouraged. There should be an up to date literature review, and meaningful comparisons with previous work to demonstrate any proposed advance. Advancements must be rigorously demonstrated by all relevant and applicable scientific means - be it mathematical proofs, statistically significant and reproducible experimental tests, field demonstrations, or whatever may be needed to convince a duly skeptical, critical scientist.

The five fundamental questions implicitly asked to IJRR authors are: "Why is this problem important?", "Why wasn't it solved before?", "What's the key idea in the solution?", "How do you show that it really works?", and "How can others use your results?"

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Manuscript Submission Guidelines: The International Journal of Robotics Research

This Journal is a member of the Committee on Publication Ethics .

Please read the guidelines below then visit the Journal’s submission site https://mc.manuscriptcentral.com/ijrr to upload your manuscript. Please note that manuscripts not conforming to these guidelines may be returned .

Only manuscripts of sufficient quality that meet the aims and scope of The International Journal of Robotics Research (IJRR) will be reviewed.

Please refer to these Guidelines for Editors and Reviewers for detailed information on the IJRR peer review process, timelines and best practice.

There are no fees payable to submit or publish in this Journal. Open Access options are available - see section 3.3 below.

As part of the submission process you will be required to warrant that you are submitting your original work, that you have the rights in the work, that you are submitting the work for first publication in the Journal and that it is not being considered for publication elsewhere and has not already been published elsewhere, and that you have obtained and can supply all necessary permissions for the reproduction of any copyright works not owned by you.

What do we publish? 1.1 Aims & Scope 1.2 Article types 1.3 Writing your paper 1.4 Paper Submission Steps and Timeline for Authors 1.5 Proposing a Review Paper 1.6 Proposing a Special issue
Editorial policies 2.1 Peer review policy 2.2 Authorship 2.3 Acknowledgements 2.4 Funding 2.5 Declaration of conflicting interests 2.6 Research Data
Publishing policies 3.1 Publication ethics 3.2 Contributor's publishing agreement 3.3 Open access and author archiving
Preparing your manuscript 4.1 Formatting 4.2 Novelty statement 4.3 Mathematics 4.4 Style for illustrations 4.5 Multimedia 4.6 Data papers 4.7 Page length 4.8 Artwork, figures and other graphics 4.9 Supplementary material 4.10 Reference style 4.11 English language editing services
Submitting your manuscript 5.1 ORCID 5.2 Information required for completing your submission 5.3 Permissions
On acceptance and publication 6.1 Sage Production 6.2 Online First publication 6.3 Access to your published article 6.4 Promoting your article
Further information

1. What do we publish?

1.1 Aims & Scope

Before submitting your manuscript to The International Journal of Robotics Research (IJRR), please ensure you have read the journal’s Aims & Scope .

1.2 Article Types

The International Journal of Robotics Research publishes

Original articles of archival value, produced to advance science and technology in the field, and stay valuable in time. The work must be original, solid, and useful for others to build upon;
Review articles on selected topics of broad interest. Review articles are normally solicited by the Editorial Board. Proposals of an unsolicited review article should be preliminarily sent to the Editor in Chief for pre-evaluation;
Special Issues on research areas of high interest. Special issues are normally solicited by the Editorial Board. Proposals of special issue should be preliminarily sent to the Editor in Chief for pre-evaluation;
Data papers of high quality, accompanied by adequate text material to illustrate the datasets and their usage in the form of a regular manuscript;
Multimedia extensions (mostly video or data) are most welcome parts of an IJRR article, concurring to illustrate and demonstrate its results.

1.3 Writing your paper

The Sage Author Gateway has some general advice and on how to get published , plus links to further resources.

1.3.1 Make your article discoverable

When writing up your paper, think about how you can make it discoverable. The title, keywords and abstract are key to ensuring readers find your article through search engines such as Google. For information and guidance on how best to title your article, write your abstract and select your keywords, have a look at this page on the Gateway: How to Help Readers Find Your Article Online .

1.4 Paper Submission Steps and Timeline for Authors

After authors submit their MS (day 0), they should expect to receive a first decision within ca. 3 months from submission (more precisely, by day 97). Possible decisions at this first stage are “Accept”, “Conditional Accept”, “Revise and Resubmit”, or “Reject”.

If the first decision is “Accept” authors must send in the final version of their manuscript within two weeks (i.e., by day 111). The production process is started.
If the first decision is “Conditional Accept” authors submit a minor revision of their manuscript along with a letter of response to review comments within two weeks (i.e. by day 111). The revised manuscript undergoes an editorial check. After this check, a second decision is issued (by day 137), which can only be “Accept” or “Reject”.
If such second decision is Accept, authors must send in the final version of their manuscript within two weeks (by day 151), and the production process is started.
If the first decision is “Revise and Resubmit”, authors submit a major revision of their manuscript along with a letter of response to review comments within a month (i.e. by day 127). The manuscript undergoes a second review round, involving the previous Senior and Associate Editors and Reviewers, and possibly new experts as needed. After this second review round, a second decision is issued within two months (by day 186), which can only be “Accept”, “Conditionally Accept”, or “Reject”.
If the second decision is Accept, authors must send in the final version of their manuscript within two weeks (day 200), and the production process is started.
If the second decision is Conditional Accept, authors submit a minor revision version of their manuscript along with a letter of response to review comments within two weeks (i.e. by day 200). The manuscript undergoes an editorial check. After this check, a third decision is issued (by day 219), which can only be “Accept” or “Reject”.
If such third decision is Accept, authors must send in the final version of their manuscript within two further weeks (by day 233), and the production process is started.

1.5 Proposing a Review Paper

Those who would like to propose a Review Paper to be published in IJRR should send an email to the EiC in advance, who will discuss with the Senior Editorial Board. The proposal of a RP should include:

The topic and tentative title of the review paper and the proposed submission deadline
The author(s), their background and qualifications. Typically we expect authors of review papers to be authoritative scholars in the field, with a strong track record of well-cited publications in the area
A motivation for timing: why a review on this subject is timely?
A list of other surveys on similar topics that appeared in IJRR or other journals in the past 10 years, and a discussion of differences: why do we need a new RP on this topic?

If encouraged by the Editorial Board, the author will prepare the final version of their Review paper and submit it as a regular IJRR manuscript., undergoing the regular review process.

1.6 Proposing a Special Issue

Those who would like to propose a Special Issue (SI) to be published in IJRR should send an email to the EiC in advance, who will discuss with the Senior Editorial Board. The proposal of a SI should include:

The topic and tentative title of the SI and the proposed submission deadline
The Guest Editor(s), their background and qualifications. Typically we expect GEs to be authoritative scholars in the field of the SI, with at least few well-cited paper in the area
A motivation for timing: why now? A motivating event could be e.g. a successful workshop, a particularly hot debate ongoing in the community, etc.
A list of other Special Issues on similar topics that appeared in IJRR or other journals in the past 10 years, and a discussion of differences: why do we need a new SI on this topic?
A list of perspective authors
At least six letters (emails are OK) from authors explicitly committing to contribute a manuscript to the proposed SI by the deadline, with title and abstract.

If accepted, a SI submission channel will be open in IJRR submission system and a deadline set. Guest Editors are introduced in the Review Management system, and they will participate in paper assignment and review, under the supervision of an IJRR Senior Editor.

If the outcome of the review process finally produces at least 5 accepted manuscripts for the SI, the Special Issues goes in production containing the manuscripts and a Guest Editorial Article provided by the Guest Editors

If less than 5 manuscripts are finally accepted, the Special Issue is not retained. Accepted manuscripts will be published as regular manuscripts in regular IJRR issues.

2. Editorial policies

2.1 Peer review policy

The International Journal of Robotics Research exclusively operates on the basis of peer reviews, with no professional editor external to the research community judging on scientific matters.

Submissions to the journal are assessed by at least two independent expert referees of appropriate standing in the field of robotics research, who make recommendations on the suitability of the articles for publication. Articles are also assessed by an Associate Editor, a Senior Editor, and the Editor in Chief before a final decision is made.

Our refereeing process is single-anonymize, that is, the referees remain anonymous and their identities are not released to authors. The referees, however, are informed of the authors’ names and affiliations.

We are committed to providing timely assessment of articles and authors are informed of the publication decision as soon as possible. Our target submission-to-decision time is 90 days in average, and 240 days in the worst case (including author’s revisions) .

According to policies by the Committee on Publication Ethics , IJRR does not permit the use of author-suggested (recommended) reviewers at any stage of the submission process, be that through the web based submission system or in other communication.

2.2 Authorship

Papers should only be submitted for consideration once consent is given by all contributing authors. Those submitting papers should carefully check that all those whose work contributed to the paper are acknowledged as contributing authors.

The list of authors should include all those who can legitimately claim authorship. This is all those who:

Made a substantial contribution to the concept or design of the work; or acquisition, analysis or interpretation of data,
Drafted the article or revised it critically for important intellectual content,
Approved the version to be published,
Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content.

Authors should meet the conditions of all of the points above. When a large, multicentre group has conducted the work, the group should identify the individuals who accept direct responsibility for the manuscript. These individuals should fully meet the criteria for authorship.

Acquisition of funding, collection of data, or general supervision of the research group alone does not constitute authorship, although all contributors who do not meet the criteria for authorship should be listed in the Acknowledgments section. Please refer to the International Committee of Medical Journal Editors (ICMJE) authorship guidelines for more information on authorship.

Please note that AI chatbots, for example ChatGPT, should not be listed as authors. For more information see the policy on Use of ChatGPT and generative AI tools .

2.3 Acknowledgements

All contributors who do not meet the criteria for authorship should be listed in an Acknowledgements section. Examples of those who might be acknowledged include a person who provided purely technical help, or a department chair who provided only general support.

Any acknowledgements should appear first at the end of your article prior to your Declaration of Conflicting Interests (if applicable), any notes and your References.

2.3.1 Third party submissions Where an individual who is not listed as an author submits a manuscript on behalf of the author(s), a statement must be included in the Acknowledgements section of the manuscript and in the accompanying cover letter. The statements must:

Disclose this type of editorial assistance – including the individual’s name, company and level of input
Identify any entities that paid for this assistance
Confirm that the listed authors have authorized the submission of their manuscript via third party and approved any statements or declarations, e.g. conflicting interests, funding, etc.

Where appropriate, Sage reserves the right to deny consideration to manuscripts submitted by a third party rather than by the authors themselves.

2.3.2 Writing assistance

Individuals who provided writing assistance, e.g. from a specialist communications company, do not qualify as authors and so should be included in the Acknowledgements section. Authors must disclose any writing assistance – including the individual’s name, company and level of input – and identify the entity that paid for this assistance. It is not necessary to disclose use of language polishing services.

2.4 Funding

The International Journal of Robotics Research requires all authors to acknowledge their funding in a consistent fashion under a separate heading. Please visit the Funding Acknowledgements page on the Sage Journal Author Gateway to confirm the format of the acknowledgment text in the event of funding, or state that: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

2.5 Declaration of conflicting interests

It is the policy of The International Journal of Robotics Research to require a declaration of conflicting interests from all authors enabling a statement to be carried within the paginated pages of all published articles.

Please ensure that a ‘Declaration of Conflicting Interests’ statement is included at the end of your manuscript, after any acknowledgements and prior to the references. If no conflict exists, please state that ‘The Author(s) declare(s) that there is no conflict of interest’. For guidance on conflict of interest statements, please see the ICMJE recommendations here .

2.6 Research Data

The journal is committed to facilitating openness, transparency and reproducibility of research, and has the following research data sharing policy. For more information, including FAQs please visit the Sage Research Data policy pages

Subject to appropriate ethical and legal considerations, authors are encouraged to:

share your research data in a relevant public data repository
include a data availability statement linking to your data. If it is not possible to share your data, we encourage you to consider using the statement to explain why it cannot be shared.
cite this data in your research

3. Publishing Policies

3.1 Publication ethics

Sage is committed to upholding the integrity of the academic record. We encourage authors to refer to the Committee on Publication Ethics’ International Standards for Authors and view the Publication Ethics page on the Sage Author Gateway .

3.1.1 Plagiarism

The International Journal of Robotics Research and Sage take issues of copyright infringement, plagiarism or other breaches of best practice in publication very seriously. We seek to protect the rights of our authors and we always investigate claims of plagiarism or misuse of published articles. Equally, we seek to protect the reputation of the journal against malpractice. Submitted articles may be checked with duplication-checking software. Where an article, for example, is found to have plagiarised other work or included third-party copyright material without permission or with insufficient acknowledgement, or where the authorship of the article is contested, we reserve the right to take action including, but not limited to: publishing an erratum or corrigendum (correction); retracting the article; taking up the matter with the head of department or dean of the author's institution and/or relevant academic bodies or societies; or taking appropriate legal action.

3.1.2 Prior publication

Material which has been previously published in archival publications is not generally acceptable for publication in a SAGE journal. Please refer to the guidance on the SAGE Author Gateway .

However, there are certain circumstances where previously published material can be considered for publication.

IJRR welcomes posting of preprint versions of an article on the author's personal or institutional website or on community preprint servers such as ArXiv. Preprints are not regarded as prior publication. Authors should disclose details (DOI, licensing terms) of preprint posting in the Novelty Statement accompanying the submission.

Should authors post or update a preprint version of a manuscript that was revised after receiving feedback from the IJRR Board, it is expected that they acknowledge it in the preprint. When a manuscript is accepted and published in IJRR, it is required that the authors update the pre-print with a publication reference, including the DOI and a URL link to the published version of the article on the journal website.

Conference proceedings

IJRR also accepts submissions containing material previously appeared in conference proceedings. In this case, the IJRR submission should provide a substantial extension of results, methodology, analysis, conclusions and/or implications over the conference proceedings paper. An extension is considered substantial if it offers new research results, methodology, analysis, conclusions and/or implications. The mere inclusion of more details, experiments, or discussion is typically considered not substantial. The final decision on what constitutes a substantial extension will be made by the Editorial Board.

Details of previous submissions (including the DOI and licensing terms) must be openly disclosed in the Novelty Statement accompanying the submission to IJRR, and all necessary permissions to re-use previously published material and attribute appropriately must be obtained by authors. Failure to disclose previously submitted material does not comply with IJRR’s code of ethics and will lead to exclusion from review.

The manuscript submitted to IJRR must contain a statement offering an open discussion of the differences with previous conference version(s), and explicitly cite the reference(s). The conference version(s) must be uploaded as accompanying material along with the journal submission.

3.1.3 Prior submission

It is not acceptable that manuscripts are submitted to IJRR while they are being evaluated by other archival Journals. In case of parallel submission of partly overlapping material to a non-archival conference or workshop, this should be openly disclosed at the time of IJRR submission.

It is also not acceptable to submit to IJRR manuscripts which have been previously rejected anywhere else, without openly informing and discussing how the reviews received from other members of the same community have been used to improve the quality of the paper. Proper practice is to enclose all relevant materials from previous submission(s) with the IJRR submission, as supplemental files. These include information on the venue of previous submission(s), the reviews received, the answers to such reviews, and the highlights of changes in the new manuscript that address the criticisms received. This material can be prepared in a similar style as when preparing a revised version for the same Journal.

Manuscripts submitted elsewhere without informing the Editorial Board nor following the above practices will be editorially rejected before review. The Editorial Board of IJRR will inform the EiC and Board of other involved Journals of such decisions.

3.1.4 Human and Animal Studies

Following SAGE’s standard policy (as spelled out at https://uk.sagepub.com/sites/default/files/editor_guidelines.pdf ), IJRR requires every manuscript involving human and animal studies to include appropriate statements on the following: (1) Ethics committee, institutional review board (IRB) or institutional animal care and use committee (IACUC) consideration. (2) Informed consent (for inclusion, collection/use of data or samples, and/or publication, as applicable) or, in the case of animal studies, animal welfare.

3.2 Contributor's publishing agreement

Before publication, Sage requires the author as the rights holder to sign a Journal Contributor’s Publishing Agreement. Sage’s Journal Contributor’s Publishing Agreement is an exclusive licence agreement which means that the author retains copyright in the work but grants Sage the sole and exclusive right and licence to publish for the full legal term of copyright. Exceptions may exist where an assignment of copyright is required or preferred by a proprietor other than Sage. In this case copyright in the work will be assigned from the author to the society. For more information please visit the Sage Author Gateway .

3.3 Open access and author archiving

The International Journal of Robotics Research offers optional open access publishing via the Sage Choice programme and Open Access agreements, where authors can publish open access either discounted or free of charge depending on the agreement with Sage. Find out if your institution is participating by visiting Open Access Agreements at Sage . For more information on Open Access publishing options at Sage please visit Sage Open Access . For information on funding body compliance, and depositing your article in repositories, please visit Sage’s Author Archiving and Re-Use Guidelines and Publishing Policies .

4. Preparing your manuscript for submission

4.1 Formatting

The preferred format for your manuscript is LaTeX. Word is also acceptable.

(La)TeX guidelines

We welcome submissions of LaTeX files. Please download the Sage LaTex Template , which contains comprehensive guidelines. The Sage LaTex template files are also available in Overleaf , should you wish to write in an online environment. If you have used any .bib or .bst files when creating your article, please include these with your submission so that we can generate the reference list and citations in the journal-specific style. If you have any queries, please consult our LaTex Frequently Asked Questions .

Microsoft Word guidelines

There is no specific template provided to submit your manuscript in Word. However, please ensure your heading levels are clear, and the sections clearly defined. The final appearance of the manuscript should resemble the two-column style typical of IJRR printed papers.

For further instructions please see the Manuscript Submission Guidelines page of our Author Gateway.

4.2 Novelty statement

During the submission process authors will be required to provide a novelty statement to accompany their submission to IJRR. Compliance is essential.

As part of the submission process please include a short statement of no more than 80 words summarising the contributions made by the paper including the reasons your paper is novel and of specific relevance to IJRR’s Aims & Scope. The purpose of this statement purpose is different from the paper’s abstract. This novelty statement will be considered by the Editors when assigning your paper for peer review and only papers with justifiable novelty statements will be moved to the next stage of the peer review process.

In case the submitted material contains parts which are already public – e.g. previously appeared in conference proceedings or in public repositories, such as arXiv – authors must include in the Novelty Statement all details (including DOI and licensing terms), along with a clear discussion of the original contribution of the submitted IJRR paper. Notice also that all necessary permissions to re-use and appropriately attribute previously published material and must be obtained by authors.

4.3 Mathematics

Type mathematical copy exactly as it should appear in print. Journal style for letter symbols is as follows: variables, italic type; constants, roman text type; matrices and vectors, boldface type. Indicate best breaks for equations in case they will not fit on one line.

4.4 Style for illustrations

A sharp image and good contrast are essential for quality reproduction. Keep in mind that most illustrations will be reproduced in a 3" column width. Show only essential information on charts and graphs, for example, coordinate axis, major grid lines, and lines on points of interest.

Provide captions for all illustrations. Label them clearly and concisely (Fig1a, Fig10, etc.).

4.5 Multimedia

Multimedia (mostly video, data, or code) extensions are most welcome parts of an IJRR paper, as they concur to illustrate and demonstrate its results.

For video extensions, authors should provide material which: i) convey a clear message related to the paper and are explicitly cited in the manuscript; ii) are of reasonable length (2 min. max. recommended); iii) have a title frame with the sentence “Extension to the IJRR manuscript titled:”, the paper title, and the authors; iv) make the material available to reviewers in the format recommended in the submission site.

For data extensions, similar guidelines as for data papers apply. Notice however that data provided as extensions are intended to reinforce the scientific/technological value of a manuscript, not as the main object of publication per se (as is the case for Data papers).

Instructions on how to submit Multimedia Extensions can be found here: Multimedia Extension Submission Guidelines .

An example of video to appear as IJRR Multimedia extension can be found here: Example IJRR video .

4.6 Data papers

IJRR also publishes high quality, peer reviewed datasets, accompanied by adequate text material to illustrate them and their usage in the form of a regular manuscript. A data paper published in IJRR must be placed in the context of current research making it clear which research field it applies to. Authors are strongly encouraged to reference related work and describe which existing community would benefit from the data. Authors should demonstrate the legibility and usability of their datasets, and provide adequate guarantees as to availability of data in repositories for a minimum of ten years.

Papers accompanying data sets are short submissions that support and summarize a substantial archival data set. Both the data set and the paper are peer reviewed with the same diligence that regular submissions receive. The contribution is expected to be in the quality and utility of the data to the robotics community.

Instructions on what is defined as a Data Paper and how to submit can be found here: Data Paper Submission Guidelines .

4.7 Page length

The normal length of an IJRR paper is 12 pages in the final, two-column format. Substantially shorter or longer submissions may be considered only if they are of sufficient merit.

4.8 Artwork, figures and other graphics

For guidance on the preparation of illustrations, pictures and graphs in electronic format, please visit Sage’s Manuscript Submission Guidelines .

Figures supplied in colour will appear in colour online regardless of whether or not these illustrations are reproduced in colour in the printed version. For specifically requested colour reproduction in print, you will receive information regarding the costs from Sage after receipt of your accepted article.

4.9 Supplementary material

This journal is able to host additional materials online (e.g. datasets, podcasts, videos, images etc) alongside the full-text of the article. For more information please refer to our guidelines on submitting supplementary files .

4.10 Reference style

The International Journal of Robotics Research adheres to the Sage Harvard reference style. View the Sage Harvard guidelines to ensure your manuscript conforms to this reference style.

Please note: While observing Harvard reference style, we do ask that you include all names in the references. ‘Et al’ should not be included in any references.

If you use EndNote to manage references, you can download the Sage Harvard EndNote output file .

4.11 English language editing services

Authors seeking assistance with English language editing, translation, or figure and manuscript formatting to fit the journal’s specifications should consider using Sage Language Services. Visit Sage Language Services on our Journal Author Gateway for further information.

5. Submitting your manuscript

The International Journal of Robotics Research is hosted on Sage Track, a web based online submission and peer review system powered by ScholarOne™ Manuscripts. Visit https://mc.manuscriptcentral.com/ijrr to log in and submit your article online.

IMPORTANT: Please check whether you already have an account in the system before trying to create a new one. If you have reviewed or authored for the journal in the past year it is likely that you will have had an account created. For further guidance on submitting your manuscript online please visit ScholarOne Online Help .

As part of our commitment to ensuring an ethical, transparent and fair peer review process Sage is a supporting member of ORCID, the Open Researcher and Contributor ID . ORCID provides a unique and persistent digital identifier that distinguishes researchers from every other researcher, even those who share the same name, and, through integration in key research workflows such as manuscript and grant submission, supports automated linkages between researchers and their professional activities, ensuring that their work is recognized.

The collection of ORCID iDs from corresponding authors is now part of the submission process of this journal. If you already have an ORCID iD you will be asked to associate that to your submission during the online submission process. We also strongly encourage all co-authors to link their ORCID ID to their accounts in our online peer review platforms. It takes seconds to do: click the link when prompted, sign into your ORCID account and our systems are automatically updated. Your ORCID iD will become part of your accepted publication’s metadata, making your work attributable to you and only you. Your ORCID iD is published with your article so that fellow researchers reading your work can link to your ORCID profile and from there link to your other publications.

If you do not already have an ORCID iD please follow this link to create one or visit our ORCID homepage to learn more.

5.2 Information required for completing your submission

You will be asked to provide contact details and academic affiliations for all co-authors via the submission system and identify who is to be the corresponding author. These details must match what appears on your manuscript. At this stage please ensure you have included all the required statements and declarations and uploaded any additional supplementary files (including reporting guidelines where relevant).

5.3 Permissions

Please also ensure that you have obtained any necessary permission from copyright holders for reproducing any illustrations, tables, figures or lengthy quotations previously published elsewhere. For further information including guidance on fair dealing for criticism and review, please see the Copyright and Permissions page on the Sage Author Gateway .

6. On acceptance and publication

6.1 Sage Production

Your Sage Production Editor will keep you informed as to your article’s progress throughout the production process. Proofs will be sent by PDF to the corresponding author and should be returned promptly. Authors are reminded to check their proofs carefully to confirm that all author information, including names, affiliations, sequence and contact details are correct, and that Funding and Conflict of Interest statements, if any, are accurate.

6.2 Online First publication

Online First allows final articles (completed and approved articles awaiting assignment to a future issue) to be published online prior to their inclusion in a journal issue, which significantly reduces the lead time between submission and publication. Visit the Sage Journals help page for more details, including how to cite Online First articles.

6.3 Access to your published article

Sage provides authors with online access to their final article.

6.4 Promoting your article

Publication is not the end of the process! You can help disseminate your paper and ensure it is as widely read and cited as possible. The Sage Author Gateway has numerous resources to help you promote your work. Visit the Promote Your Article page on the Gateway for tips and advice.

7. Further information

Any correspondence, queries or additional requests for information on the manuscript submission process should be sent to The International Journal of Robotics Research editorial office at [email protected] .

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International Foundation of Robotics Research

Publications, springer proceedings in advanced robotics (spar), springer tracts in advanced robotics (star), springer handbook of robotics, international journal of robotics research.

The Springer Proceedings in Advanced Robotics (SPAR) publishes new developments and advances in the fields of robotics research, rapidly and informally but with a high quality.

The intent is to cover all the technical contents, applications, and multidisciplinary aspects of robotics, embedded in the fields of Mechanical Engineering, Computer Science, Electrical Engineering, Mechatronics, Control, and Life Sciences, as well as the methodologies behind them.

The publications within the "Springer Proceedings in Advanced Robotics" are primarily proceedings and post-proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. Also considered for publication are edited monographs, contributed volumes and lecture notes of exceptionally high quality and interest.

An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.

The proceedings of the International Symposium of Robotic Research (ISRR) , the International Symposium of Experimental Robotics (ISER) , and the Workshop on the Algorithmic Foundations of Robotics (WAFR) are published here.

The Springer Tracts in Advanced Robotics (STAR) publish new developments and advances in the fields of robotics research, rapidly and informally but with a high quality. The intent is to cover all the technical contents, applications, and multidisciplinary aspects of robotics, embedded in the fields of Mechanical Engineering, Computer Science, Electrical Engineering, Mechatronics, Control, and Life Sciences, as well as the methodologies behind them. Within the scope of the series are monographs, lecture notes, selected contributions from specialized conferences and workshops, as well as selected PhD theses.

Special offer: For all clients with a print standing order we offer free access to the electronic volumes of the Series published in the current year.

Indexed by DBLP, Compendex, EI-Compendex, SCOPUS, Zentralblatt Math, Ulrich's, MathSciNet, Current Mathematical Publications, Mathematical Reviews, MetaPress and Springerlink.

The second edition of this handbook provides a state-of-the-art cover view on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics .

The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology.

The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team.

A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app.

Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/

A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research.

IJRR also publishes high quality, peer reviewed datasets and multimedia extensions alongside articles. This journal is a member of the Committee on Publication Ethics (COPE) . All issues of IJRR are available to browse online .

Select papers of the International Symposium of Robotic Research (ISRR) , the International Symposium of Experimental Robotics (ISER) , and the Workshop on the Algorithmic Foundations of Robotics (WAFR) are published here.

International Journal of Robotics Research

Subject Area and Category

Artificial Intelligence
Electrical and Electronic Engineering
Mechanical Engineering
Applied Mathematics
Modeling and Simulation

SAGE Publications Inc.

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02783649, 17413176

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Industrial Robot

Issue(s) available: 255 – From Volume: 1 Issue: 1 , to Volume: 51 Issue: 2

Issue 2 2024
Issue 1 2024
Issue 6 2023
Issue 5 2023
Issue 4 2023 Soft Robotics and AI
Issue 3 2023
Issue 2 2023
Issue 1 2023
Issue 6 2022
Issue 5 2022
Issue 4 2022
Issue 3 2022 Industry 4.0
Issue 2 2022
Issue 1 2022
Issue 6 2021
Issue 5 2021 Learning-based vision perception for industrial robots
Issue 4 2021
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Issue 1 2021
Issue 6 2020
Issue 5 2020
Issue 4 2020
Issue 3 2020
Issue 2 2020
Issue 1 2020
Issue 6 2019
Issue 5 2019
Issue 4 2019 Human-Robot Collaboration
Issue 3 2019 Cross-Modal Perception for Industrial Robots
Issue 2 2019
Issue 1 2019
Issue 6 2018
Issue 5 2018
Issue 4 2018
Issue 3 2018
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Issue 1 2018
Issue 6 2017
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Issue 4 2017 Real-world mobile robot systems
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Issue 5 2016 Industrial Robot Agility
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Issue 3 2012 Mobile Robots
Issue 2 2012
Issue 1 2012 Designing and Managing Robotic Systems
Issue 6 2011
Issue 5 2011 Humanitarian military applications
Issue 4 2011
Issue 3 2011 CLAWAR and medical and mobile robotics
Issue 2 2011
Issue 1 2011 Applications Towards Factories of the Future
Issue 6 2010 Robots in the food industry
Issue 5 2010 Robots for NDT and inspection
Issue 4 2010 ICIRA and Micro Robots
Issue 3 2010 CLAWAR and mobile robots
Issue 2 2010 Man Machine and Brain Interfaces
Issue 1 2010 Robots for machining
Issue 6 2009 Robots in the food industry
Issue 5 2009 Humanoid Robots Prosthetics
Issue 4 2009 Mobile Robots CLAWAR
Issue 3 2009 Chinese Robots and Applications
Issue 2 2009 Chinese Robots and Applications
Issue 1 2009 Theme Title Finishing and Polishing
Issue 6 2008 Theme Title Machine Intelligence
Issue 5 2008 Theme Title Cutting Robots
Issue 4 2008 Medical Robots
Issue 3 2008 Mobile Robots
Issue 2 2008 Biotechnology and Laboratory Robots
Issue 1 2008 Cooperative and twoarm robots
Issue 6 2007 Welding Robots
Issue 5 2007 Simulation and offline programming
Issue 4 2007 Robotic Assembly
Issue 3 2007 Force sensing
Issue 2 2007 Mobile robots
Issue 1 2007 Food industry
Issue 6 2006 Robot Control and Programming
Issue 5 2006 Automotive Industry
Issue 4 2006 Mobile Robots
Issue 3 2006 Nuclear industryTeleoperation
Issue 2 2006 Composite Fabrications
Issue 1 2006 Painting
Issue 6 2005 Service Robots
Issue 5 2005 Advanced Assembly
Issue 4 2005 Welding Robots Mechatronics
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Issue 2 2005 Mobile Robots Climbing and Walking Robots
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The role of robots in logistics

The purpose of this paper is to illustrate the growing role of robots in the logistics industry.

Prototyping of compliant grippers using FFF and TPU

The purpose of this paper is to investigate the process of fused filament fabrication (FFF) of a compliant gripper (CG) using thermoplastic polyurethane (TPU) material. The paper…

Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm…

Online path planning of pork cutting robot using 3D laser point cloud

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork…

LIDAR-based SLAM system for autonomous vehicles in degraded point cloud scenarios: dynamic obstacle removal

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the…

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and…

Attitude oscillation suppression control of a XK-I spherical robot

In complex environments, a spherical robot has great application value. When the pendulum spherical robot is stopped or disturbed, there will be a periodic oscillation. This…

Impedance control for force tracking of a dual-arm cooperative robot based on particle swarm optimization

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the…

SiLK-SLAM: accurate, robust and versatile visual SLAM with simple learned keypoints

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios…

Design and experiment of transmission tower climbing robot inspired by inchworm

The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power…

Calibration strategies for enhancing accuracy in serial industrial robots for orbital milling applications

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use…

Development of a novel nonrigid support friction stir welding repair robot for aluminum alloy train

This paper aims to develop a specific type of mobile nonrigid support friction stir welding (FSW) robot, which can adapt to aluminum alloy trucks for rapid online repair.

DARLOS: a lightweight dual-arm robotic live-line operation system for autonomous high-voltage distribution grid maintenance

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line…

A robot motion skills method with explicit environmental constraints

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit…

An efficient constraint method for solving planning problems under end-effector constraints

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new…

Piston-like particle jamming for enhanced stiffness adjustment of soft robotic arm

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables…

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown…

Research on dual robot collaboration method based on improved double ant colony algorithm

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single…

Narrow gap welding seam deflection correction study based on passive vision

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection…

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous…

A heavy-load wall-climbing robot for bridge concrete structures inspection

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF…

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this…

An efficient and accurate force/torque sensing method based on an excitation trajectory

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation…

Operation space analysis and trajectory planning of mechanical arm in narrow space for GIS (gas insulated switchgear) inspection robot

The purpose of this paper is to achieve optimal climbing control of the gas-insulated switchgear (GIS) robot, as the authors know that the GIS inspection robot is a kind of…

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Foundation Models and Neural-Symbolic AI for Robotics

Special Issue in The International Journal of Robotics Research (IJRR)

Published: Feb 6, 2024 by Chen Wang

The field of robotics has witnessed unprecedented growth in the integration of machine learning models, especially with the recent advent of foundation models , neural-symbolic AI , generative models (large language models, diffusion models, etc), and other emerging learning models . These methods have revolutionized various tasks in computer vision, natural language processing, computer graphics, etc. However, their application in the context of robotics remains underexplored. This special issue (SI) is to fill this gap and bring together leading researchers and practitioners in robotics to shed light on the latest advancements, methodologies, and best practices in this interdisciplinary domain.

This SI aims to provide a comprehensive overview of the current landscape, from the underlying theories and in-depth reviews to the forward-looking perspectives, practical implementation, and real-world challenges. By fostering a holistic understanding and promoting collaboration between experts, we aspire to accelerate the adoption of these advanced models and drive the next wave of innovations in robotic systems, including but not limited to robotic perception, cognition, planning, and control.

We accept top-quality original (unpublished) articles and review papers. Each submission undergoes peer review and follows the standard IJRR review process. Please refer to the IJRR instructions for authors for more details. Papers will be selected based on their novelty, significance, and alignment with the special section’s focus.

This call requests clear contributions to robotics, any submission that doesn’t fit into the list of topics will be desk rejected. There is no page limit for IJRR submissions. The rule is however that a paper should be as long as necessary, and no longer: conciseness is highly valued.

Challenges of integrating foundation models with robotic systems.
Case studies highlighting their impact on robotic applications.
Exploration of the Integration of neural networks and symbolic systems such as logical, geometrical, and physical systems.
Real-world applications and benefits in robotics.
Techniques for effective neural-symbolic reasoning in complex robotic tasks.
Exploring the applications and challenges of integrating state-of-the-art generative models, such as large language models, diffusion models, and other novel generative methods in robotic systems.
Addressing computational demands, real-time processing needs, and context comprehension hurdles when merging generative models with robotics.
Exploration of other novel neural learning techniques in robotic systems.
Integration barriers and effective implementation strategies.
Case studies showcasing the real-world benefits to robotics.
Advanced methodologies for robotic perception using machine learning.
The role of cognition in enhancing robotic tasks.
Innovations in sensory data processing and interpretation.
Neural and neural-symbolic learning-driven control and planning mechanisms.
Neuro-symbolic reinforcement learning (RL) and its applications in robotics.
Sample complexity reduction of RL by leveraging relational knowledge.
Sequential decision-making with explainable policies.
Out-of-distribution sequential decision-making by leveraging reasoning.
Challenges in real-time decision-making and path planning.
Addressing issues and limitations related to the scalability and robustness of foundation models and neural-symbolic AI in robotics.
Ethical considerations and safety concerns.
Showcasing the practical impact of integrating these machine learning models in diverse robotic fields such as assistive and field robotics.
Lessons learned and best practices from real-world implementations.
Predictions and trends for the future of machine learning in robotics.
Potential new research areas and unexplored applications.
The evolution of hybrid models and their long-term implications in robotics.
Submission Open: Feb 6, 2024
Submission Close: Aug 11, 2024 ( No extension will be given )
Review and Revision: Feb to Dec 2024
Publication: Jan 2025

Submission Site

https://mc.manuscriptcentral.com/ijrr

Guest Editors

Chen Wang , University at Buffalo

Jiajun Wu , Stanford University

Fei Xia , Google DeepMind

Letizia Gionfrida , King’s College London

Anil Bharath , Imperial College London

Alexander Gray , IBM

Send emails directly to [email protected] .
All Guest Editors will receive your inquiries.

Sharable PDF

You may also download sharable PDF for this SI via This Link .

Frequently Asked Questions (FAQ)

1. is there a hard requirement for the submission to have real robot experiments.

The answer would depend on the type of your work. We believe that real robot experiments can provide valuable empirical evidence and validation for a submission, however, it’s not always a strict requirement. If your research is primarily focused on theoretical aspects, simulations, or other forms of experimentation, you can explain the rationale behind your chosen approach and how it contributes to the advancement of the field. However, if real robot experiments are feasible and add significant value to your work, they can strengthen your submission by demonstrating practical applicability and validating your proposed methods in real-world scenarios.

2. Do you accept submissions containing material previously appeared in conference proceedings ?

IJRR accepts submissions containing material previously appeared in conference proceedings. In this case, the IJRR submission should provide a substantial extension of results, methodology, analysis, conclusions and/or implications over the conference proceedings paper. An extension is considered substantial if it offers new research results, methodology, analysis, conclusions and/or implications. The mere inclusion of more details, experiments, or discussion is typically considered not substantial. The final decision on what constitutes a substantial extension will be made by the Editorial Board.

3. Am I allowed to submit an extended version of a conference paper which is under review ?

Conferences such as ICRA, IROS, RSS, and CoRL are considered archival. Authors need to ensure any ongoing reviews at those venues are concluded before submission.

4. Are preprints allowed?

IJRR welcomes posting of preprint versions of an article on the author’s personal or institutional website or on community preprint servers such as ArXiv. Preprints are not regarded as prior publication. Authors should disclose details (DOI, licensing terms) of preprint posting in the Novelty Statement accompanying the submission.

Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions

Regular Paper
Open access
Published: 22 April 2024

Cite this article

You have full access to this open access article

Aliki Stefanopoulou 1 , 2 ,
Emmanuel K. Raptis 1 , 2 ,
Savvas D. Apostolidis 1 , 2 ,
Socratis Gkelios 1 , 2 ,
Athanasios Ch. Kapoutsis 1 ,
Savvas A. Chatzichristofis 3 ,
Stefanos Vrochidis 1 &
Elias B. Kosmatopoulos 1 , 2

This paper focuses on Coverage Path Planning (CPP) methodologies, particularly in the context of multi-robot missions, to efficiently cover user-defined Regions of Interest (ROIs) using groups of UAVs, while emphasizing on the reduction of energy consumption and mission duration. Optimizing the efficiency of multi-robot CPP missions involves addressing critical factors such as path length, the number of turns, re-visitations, and launch positions. Achieving these goals, particularly in complex and concave ROIs with No-Go Zones, is a challenging task. This work introduces a novel approach to address these challenges, emphasizing the selection of launch points for UAVs. By optimizing launch points, the mission’s energy and time efficiency are significantly enhanced, leading to more efficient coverage of the selected ROIs. To further support our research and foster further exploration on this topic, we provide the open-source implementation of our algorithm and our evaluation mechanisms.

Avoid common mistakes on your manuscript.

1 Introduction

Nowadays, a wide range of enterprise domains take advantage of the remote sensing capabilities offered by Unmanned Air Vehicles (UAVs) to collect data for various purposes. The scope of applications that utilize UAVs for data collection includes precision agriculture (Maes and Steppe 2019 ; Krestenitis et al. 2024 ; Karatzinis et al. 2020 ; Raptis et al. 2023 ), infrastructure inspection (Máthé and Buşoniu 2015 ; Shakhatreh et al. 2019 ), exploration (Cesare et al. 2015 ; Renzaglia et al. 2020 ) and monitoring (Koutras et al. 2020 ; Kapoutsis et al. 2019 ). To efficiently collect data in an automated way, Coverage Path Planning (CPP) methodologies (Galceran and Carreras 2013 ; Cabreira et al. 2019 ) are commonly utilized to calculate paths for the UAVs to cover a user-defined Region of Interest (ROI) completely. The utilization of multiple UAVs to cover a ROI can introduce significant benefits in terms of the area that can be covered during the nominal operational duration of the vehicles used, or the amount of time needed to cover a certain ROI, two factors that may be critical in certain types of operations, such as the search and rescue missions. This kind of methodology is known as multi-robot Coverage Path Planning (mCPP) (Almadhoun et al. 2019 ).

Using multiple unmanned vehicles can greatly decrease the duration required to carry out a specific mission. However, in order to fully maximize the potential benefits of unmanned vehicles, an mCPP approach must integrate characteristics that promote overall efficiency in terms of time and energy usage.

The major factors that affect the efficiency of a CPP methodology, are:

The length of the path needed to scan a certain ROI,

The number of turns introduced in the generated path,

The number of re-visitations of specific parts of the ROI demanded to cover it completely, and

The distance of the take-off and landing position from the starting and ending points of the path respectively. Cabreira et al. ( 2019 ).

Optimizing all of these parameters for energy and time efficiency while ensuring complete coverage is a challenging task, even for methods that only have to manage a single vehicle. The situation becomes even more intricate when attempting to accomplish this task cooperatively with methods that require handling of multiple vehicles simultaneously.

The problem tackled in this work is the design of multi-robot paths that can optimally cover any user-defined ROI, even the most complex-shaped, concave ones, including No-Go-Zones (NGZs) inside of them . A distinct feature of this work is the selection of robots’ launch points by an optimization procedure, in order to further increase the energy and time efficiency of the generated trajectories. This problem is of great importance, especially i) in cases that coverage missions are performed periodically in large areas and ii) in scenarios that completing the mission as quickly as possible is crucial.

In many such cases, the initial configuration of the group does not only correspond to the first waypoints of the missions but also indicates the optimal positions to establish bases (charging/refueling points) for the unmanned vehicles that periodically cover a specific ROI.

Two indicative examples of such missions are optimizing crop yield in precision agriculture and assessing changes in urban infrastructure. In precision agriculture, real-time monitoring of crop health and nutrient levels is crucial for maximizing yield and minimizing resource use. Meanwhile, in urban infrastructure assessment, tracking long-term changes in building structures and city layouts is essential for effective planning and development.

1.1 Contributions

In this project, building upon the foundation established in Apostolidis et al. ( 2022 ), we are continuing the research previously conducted by our laboratory, introducing an optimization scheme to search for the optimal initial configuration of UAVs in a way that provides increased operational efficiency.

The base methodology already incorporates a set of features that maximizes the efficiency of the individual paths. In this project, we introduce an optimization procedure that not only reduces complexity in the shapes of the exclusive sub-regions, thereby minimizing the number of turns required in the multi-robot solution, but also enhances energy and time efficiency for the multi-robot solution. At the same time it suggests the ideal points to setup launching/docking stations for long-term coverage operations. This is accomplished by precisely controlling the launch points of each UAV. The efficiency of the proposed scheme is proved through extensive simulated evaluations where the gains are quantified and measured.

As a significant contribution to the research community, we have open-sourced the repositories containing our algorithms and evaluation mechanisms. This initiative is crucial in fostering collaboration, enabling researchers to replicate, validate, and build upon our work, thereby advancing the collective knowledge in this domain Footnote 1 .

1.2 Paper outline

The rest of the work is organized as follows: Sect. 2 presents a short literature overview of the relatively recent CPP works that focus on the efficiency of the generated trajectories, Sect. 3 strictly defines the specific problem that this work solves, Sect. 4 describes the methodology followed in an elaborate way, Sect. 5 presents the results of the simulated evaluation of the method and Sect. 6 provides an overview of the research and delves into its results and consequences.

2 Related work

The energy consumption and the operational duration of coverage missions are problems that have attracted the interest of several researchers, resulting in various studies presenting diverse approaches to address these challenges. This sub-section presents some of these works that manage to stand out, either by introducing innovative solutions to these problems or by delivering interesting/promising results.

In Choset and Pignon ( 1998 ); Bähnemann et al. ( 2021 ), the authors present a path planner for low-altitude terrain coverage in known environments utilizing a single unmanned rotary-wing micro aerial vehicle. The power of the proposed methodology lies in the fact that it can achieve complete coverage of ROIs of any shape and size, which may include a various number of no-fly zones inside it. The introduced approach extends boustrophedon coverage planning by optimizing different sweep combinations to find the optimal sweep path. It can operate using three optimization criteria, that include minimizing time, path length, and the number of turns. While this approach stands out as one of the most effective in terms of complete coverage in demanding ROIs, the strictness of the complete coverage limitation impacts the optimality in terms of time and energy efficiency that can be achieved.

Vandermeulen et al. ( 2019 ) presents an approach to solve the mCPP problem, intending to minimize the mission time, with a particular focus on factoring in the impact of the number of turns in the generated paths. To solve this problem, the presented methodology partitions the environment into a set of ranks which are long thin rectangles (having the width of the robot’s coverage tool). These ranks are oriented in a way that minimizes the occurrence of turns. As a next step, multiple traveling salesperson problems (m-TSP) are solved on the set of ranks, that intend to reduce the robots’ mission time. The coverage plan that is generated, according to the authors, provides complete coverage of the ROI. This method is mainly focused on ground vehicles used to vacuum indoor environments. However, it could be easily applied in other coverage missions with minor modifications. It should be noted that the generated paths present intersecting points among different vehicles, introducing increased probability for collisions and unnecessary overlaps, which leads to redundant multiple coverage of certain parts of the ROI, thus decreasing the overall operational efficiency.

In Ramesh et al. ( 2022 ), the authors introduce a methodology that tackles the same problem and presents results very similar (at least optically) to Vandermeulen et al. ( 2019 ), however, for a single vehicle. As the authors claim, their heuristic method for finding the number of ranks guarantees optimality. As in the previous work, the generated path has an unnecessary overlap to connect the sub-parts of the region, leading to multiple coverage of specific areas and consequently leading to decreased overall efficiency.

Skorobogatov et al. ( 2021 ) introduces an open-source solution for splitting areas of any shape for mCPP missions, tailored to UAVs. It assumes certain parameters such as the number of vehicles, the requirements of the area to be covered by each UAV, and, optionally, the initial position of each UAV. The target of the splitting procedure is to maximize the "compactness" of the generated sub-polygons. By adopting the aforementioned approach, the resulting trajectories exhibit a decreased number of turns, leading to a reduction in coverage duration. While this work presents an approach with increased efficiency for the mCPP missions, it possesses two significant disadvantages (also presented in Vandermeulen et al. ( 2019 ), but with a more pronounced impact in the context of UAV utilization). The first one is that the generated trajectories overlap and prsent several intersecting points in the marginal regions of the sub-polygons. As already mentioned, this may lead to unnecessary overlapping coverage of various parts of the ROI, thus reducing the overall efficiency. Still, most importantly, the intersecting points increase the risk of collisions among the UAVs, which is considered a major safety issue. In addition to that, due to the back-and-forth pattern used, the initial positions for the UAVs are required to be near the margins of the ROI, significantly limiting the options to establish bases in cases that the coverage missions are required to be performed regularly.

Kapoutsis et al. ( 2017 ) investigates an area division approach to decompose a ROI into an as-many-as-the-vehicles number of sub-regions. It proceeds to address the mCPP problem by solving individual single-robot CPP problems, employing the STC algorithm (Gabriely and Rimon 2001 ) for each sub-region. The presented methodology effectively resolves the mCPP problem in a computationally efficient way. At the same time, it also inherits some features of STC that can lead to increased operational efficiency (e.g., it eliminates the need for backtracking and avoids unnecessary movements that do not contribute to the coverage process).

Gao et al. ( 2018 ) uses (Kapoutsis et al. 2017 ) as a basis and, by utilizing an improved ant colony optimization (ACO) algorithm, it attempts to construct the best-spanning trees to obtain paths with the minimal number of turns, which contributes to minimizing the energy/time consumption. This, combined with the features inherited by STC, leads to a method that presents increased operational efficiency when applied to a single robot. However, due to the complex shapes that the generated sub-regions showcase, the number of turns, and thus the overall efficiency, may not reach its optimal level when multiple vehicles are employed.

Apostolidis et al. ( 2022 ) also builts uppon (Kapoutsis et al. 2017 ) and presents an end-to-end platform for multi-UAV remote sensing coverage missions, focusing on real-life applicability and efficiency. One of the most significant contributions of this work is the optimization procedure introduced to calculate the optimal grid, which maximizes coverage in real-life problems for grid-based methodologies. Furthermore, a turns reduction procedure is applied on the individual STC paths, leading in a substantial decrease in both the number of turns and the overall operational time. This method also inherits the nice-to-have features that STC paths provide; However, as Gao et al. ( 2018 ), it also suffers from the complexity of the sub-regions shapes that introduce multiple turns to the paths, when multiple vehicles are deployed.

Finally, Luna et al. ( 2022 ) deals with the problem of fast mCPP for UAVs. The presented work is packed to ensure that it is easily usable by first responder. To solve the mCPP problem, three different methods are utilized. Out of them, "POWELL-BINPAT" is the most efficient regarding the mission’s duration. Contrary to the aforementioned works, this methodology ensures that there are no overlapping trajectories in the generated mission’s paths. In addition to that, the authors put forth a solution that involves flying each UAV at different altitudes, from the launching point to the first mission’s waypoint and from the last waypoint back to the ground, to eliminate the risk of collisions and provide safety for multi-UAV operations. However, once again, the paths’ initial points are challenging to control, limiting the possible positions significantly to establish charging points inside the operational area in cases where coverage missions are performed regularly.

Out of the works presented above, Choset and Pignon ( 1998 ); Bähnemann et al. ( 2021 ); Apostolidis et al. ( 2022 ); Luna et al. ( 2022 ) are the most mature ones, in terms of real-life operation readiness. All of the works described propose interesting ideas to reduce operational time and energy consumption, however, they also seem to incorporate several disadvantages. Specifically, none of the above works manage to integrate all of the essential features required to achieve optimal operational efficiency in the context of long-term, real-world coverage operations. They do not generate paths with minimized length, reduced number of turns, decreased operational time, and lower energy consumption, both for the single and multi-robot solutions. Moreover, they do not guarantee the safety of vehicles by preventing trajectory intersections and they do not facilitate the placement of launching and docking stations within the region of interest without adding unnecessary distance for vehicles to traverse when reaching the first and returning from the last waypoints.

3 Problem formulation

Let’s consider a Region of Interest (ROI), which may include no-go-zones (NGZs) and obstacles, that needs to be fully covered using a group of UAVs. The objective for this group of UAVs is to work collaboratively in order to achieve complete coverage of the ROI in the shortest possible time, effectively maximizing the utilization of all available resources. For this reason, meticulous path planning for the group during the mCPP mission is essential. Our goal is to generate mCPP trajectories which ensure that each UAV operates efficiently, avoiding collisions with obstacles and staying clear of NGZs. Additionally, the designed trajectories should prioritize efficiency, in terms of both time and operational resources. By carefully optimizing the paths, we not only intend to maximize the coverage of the area of interest, but also make the most effective use of available resources, leading to an effective and resource-efficient mission execution.

The methodology presented in this work, builds upon some previous works from our lab, that have already solved the problems of (i) efficiently representing a region on a grid, taking into consideration the parameters that affect the effectiveness of real-world operations, (ii) task allocation of the overall problem, so that each member of the UAV group can undertake a specific part of the overall ROI to cover, and (iii) generation of trajectories for each of them, to fulfill their objective and cover their exclusive sub-part of the ROI. The following sub-section shortly presents these steps, while more technical details about them can be found in Appendix A.

3.1 ROI representation on grid, task allocation and path planning

Given as input: (i) the user-defined ROI and NGZs, formatted in the WGS84 coordinate system (Cai et al. 2011 ), (ii) the desired distance between sequential trajectories (scanning density - \(d_s\) ), (iii) the number of UAVs, and (iv) their initial positions in the operational area, the following steps take place:

The ROI is represented on a grid, with the size of the grid and the length of its cells depending on the \(d_s\) . Each cell of the grid acquires a state that can be "Obstacle," "Free Space," or "Launch Point".

DARP algorithm (Kapoutsis et al. 2017 ), using as input the representation of the ROI on grid, runs to generate exclusive sub-regions for each UAV to cover.

STC algorithm (Gabriely and Rimon 2001 ) is utilized to generate coverage trajectories for each UAV to follow, so that it completely covers its operational sub-region.

3.2 Path execution

The UAVs navigate through the designed paths within the ROI, described using intermediate nodes in the grid. The aforementioned paths are defined using the following Equation:

In Eq. 1 , \((p_0,..., p_n) \in P\) are the paths that each one of the UAVs will navigate through, \((n_0^0,n_0^1,..., n_0^l)\) and \((n_n^0,n_n^1..., n_n^k)\) are the intermediate nodes that the UAVs will pass through to execute their assigned path, with \(l, k\) set equally due to the equal distribution of coverage areas among UAVs. Therefore, \(n_{i}^0 \in p_{i}, i=0,...n\) denotes the launch points in the grid for the \(i_{th}\) UAV.

3.3 Evaluation metrics

To assess the effectiveness of the designed paths and their energy efficiency, we introduce the following evaluation metric:

In Eq. 2 , \(t_{mission}\) represents the total mission time for the UAV group, \(P\) is the set of paths assigned to the group, \(p_{i}, i = 1,..., n\) is the path assigned to the \(i_{th}\) UAV in the group and \(p_i(t)\) is the time it takes for the \(i_{th}\) UAV to complete its designated path.

3.4 Path time calculation

In mCPP problems, it is a common simplification to assume that all robots travel at the same speed. This assumption serves in estimating task completion times, which is valuable for mission planning, scheduling and optimization purposes. Thus, the time required for a single UAV to execute its designated path depends on the time it takes for the UAV to travel along its assigned path and the time it takes to execute the turns within the path, as expressed in the following equation:

In Eq. 3 , \(t_{straight}\) is the time required for the UAV to execute the straight segment of its path, \(n_{turns}\) are number of turns along the path and \(t_{turn}\) is the time required to complete one turn, which includes the time needed for the UAV to slow down before taking the turn and accelerate again after completing it.

3.5 Minimizing mission time

From Eqs. 2 and 3 , it becomes clear that in order to minimize the total time spent on the mission, the variables we need to take under consideration are the time needed for the UAVs in the group to execute the straight portion of their assigned paths and the total number of turns \(n_{turns}\) in each one of the UAVs’ paths.

At each cell, the UAV has two options: to travel straight, resulting in two edges per cell, or to turn, which also results in two edges per cell. Since the designated area is equally divided among the UAVs, each UAV is assigned an equal number of cells. Consequently, the path length is deemed identical among all UAVs in the group. Thus, the less the total number of turns in the resulted path, the less time takes for the UAV to complete it. Fewer turns also means that real robots get stuck less often and have improved localization (I. Vandermeulen and Kolling 2019 ).

3.6 Controllable variables

Inheriting the DARP optimality about the resulting paths (Kapoutsis et al. 2017 ), the only open variable that seems to affect the number of turns and therefore the overall performance in the mCPP problem is the launch points of the UAVs, defined as follows:

In Eq. 4 , \(lp\) is the set Launch Points of the UAVs in the group within the grid.

The resulting paths are guaranteed by the DARP algorithm to be optimal, according to the division of the area that it achieves (Kapoutsis et al. 2017 ). However, different Launch Points for the UAVs within the Grid result to different area division, which strongly affects the morphology of the resulted paths, and thus, the execution time of mCPP the mission, as depicted in Fig. 1 .

Different Launch points may result in paths that contain different number of turns

3.7 Decision variables

The overall mission duration for the group strongly depends on the highest count of turns observed among the designated paths of the UAVs and can be expressed as:

In Eq. 5 , \(n_{i}, = 1, \ldots , n\) denotes the turns present in each respective path.

Given a finite set of paths, \(P\) , our optimization objective is to select a set of paths that result in the minimum execution time of the mission. Thus, this optimization problem is described in the following Equation:

In Eq. 6 , \(J(T_{mission})\) signifies the objective of minimizing the mission execution time \(T_{mission}\) , and \(argmin_{P}\) denotes the selection of paths from the finite set \(P\) that achieves this minimum mission time.

3.8 Operational constraints

Applying the decision vector \(lp\) derived from Eq. 4 on the DARP algorithm (Kapoutsis et al. 2017 ) results in a fair area division between the UAVs and a resulting path of equal total length for each UAV that ensures complete coverage of its assigned area.

Moreover, the set of nonlinear constraints in Eq. 4 , which must be held for each new robots’ configuration, include the following:

All UAVs should remain within the operational area boundaries, i.e. within \([xmin, xmax]\) and \([ymin, ymax]\) in the x-and y-axes, respectively.

The launch point of each UAV should be different from the positions of the other UAVs in the group, i.e. \(\forall i, j \in UAVs_{i, j}, i\ne {j}, lp_{i} \ne lp{j}\) .

The launch point of each UAV should not be on areas of the grid that are either occupied by static obstacles or considered no-fly zones, i.e. \(\forall i, j \in UAVs_{i, j}, \forall O \in grid, i\ne {j}, LP_{i} \ne O\) , where \(O\) represents an occupied cell on the grid.

3.9 Optimization problem

Given the mathematical description presented above, the problem of choosing the launch points for a multi-UAV system to minimize the maximum number of turns can be described by the following constrained optimization problem:

The optimization mentioned above cannot be tackled using traditional gradient-based algorithms because the explicit form of the \(J\) function is unavailable. As a result, we are dealing with a non-linear, nonconvex, integer programming optimization problem.

4 Methodology

After applying the DARP methodology in the mCPP setup, the problem is translated to the optimization problem that is defined in Eq. 7 . Since the problem we are dealing with is typically met in the real world, the goal is to obtain a Pareto optimal solution within a given evaluation budget. Generally, objective functions for real-world problems, such as the one described in Eq. 7 , like greedy search algorithms, are not time-efficient and require thousands of evaluations to converge. Additionally, utilizing a random search to find optimal parameters does not ensure convergence within a limited evaluation budget. Therefore it is crucial to approach such problems using more computationally efficient algorithms that are more likely to converge in a reasonable number of evaluations.

In our pursuit of searching for the optimal UAVs’ launch points for this work, we found ourselves faced with the crucial decision of selecting the most suitable optimization algorithm. After careful consideration and a thorough review of the available options, we made the deliberate choice to employ a tree-structured Parzen estimator (TPE) (Bergstra et al. 2011 ) as our primary tool to assist in this task.

Our selection of TPE (Bergstra et al. 2011 ), was driven by several compelling reasons. First and foremost, TPE is knowm for its effectiveness in machine learning methodologies and Neural Architecture Search (NAS). Its track record of success in these domains made it a natural contender for our study. One of the most important features of TPE is its ability to significantly reduce the total number of evaluations of the objective function. This is of utmost significance, as exhaustive evaluation of parameter configurations can be very time-consuming and resource-intensive. TPE accomplishes this efficiency by dedicating more time to configuring and evaluating the most promising sets of input parameters, guided by the insights gained from previous evaluations. This strategy allows us to converge towards optimal launch points with fewer iterations, making the optimal mCPP objective more time-efficient and cost-effective.

Another remarkable attribute of TPE is its versatility in handling various types of parameters, including discrete, which is the specific scenario we encounter in our case. Central to TPE’s success is its utilization of a surrogate function. This function serves as a probabilistic representation of the objective function we intend to optimize. It is constructed using information gathered from previous evaluations, enabling us to make informed decisions about which parameter configurations are most likely to yield the launch points that result in the minimum number of turns in the mCPP mission.

4.1 Selection function

In our work, we utilize TPE so as to maximize the Expected Improvement concerning a set of launch points for the UAVs. Expected Improvement is the expectation that under our model \(M\) of \(f: lp \rightarrow \Re ^{N}\) , that \(f(lp)\) will exceed a threshold of turns \(T^{*}\) , as described in the following Equation:

In Eq. 8 , \(T^{*}\) is a threshold value of the objective function, \(lp\) (launch points) are the selected launch points of the UAVs, \(T\) is the actual number of Turns using parameters \(lp\) , and \(p_M(T|lp)\) is the surrogate probability model, expressing the probability of \(T\) turns given the selected launch points \(lp\) .

In Eq. 8 , \(p_M(T|lp)\) is not directly represented, but instead, we use:

In Eq. 9 , \(p(lp|T)\) is the probability of the launch points given the number of turns, \(T\) , in the paths that the DARP algorithm generates.

Assuming a set of observations that takes {( \(lp^{(1)}\) , \(T^{(1)}\) ),..., ( \(lp^{(k)}\) , \(T^{(k)}\) )}, \(p(lp|T)\) can be expressed by two probability density functions:

In Eq. 10 , \(T<T^{*}\) is the lower value of the objective function than the threshold, \(l(lp)\) is the probability density function formed using the observed variables { \(lp^{(i)}\) } such that \(T^{*}>T^{(i)}\) and \(g(lp)\) is the probability density function using the remaining observations. These two models are tree-structured hierarchical processes constructed using adaptive Parzen estimators, as presented in Bergstra et al. ( 2011 ).

The probability that \(T\) (the number of turns) is less than \(T^{*}\) , \(\gamma\) , is defined as:

and by construction:

\(\int _{-\infty }^{T^*}(T^{*}-T)p(lp|T)p(T)dT = l(lp)\int _{-\infty }^{T^*}(T^*-T)p(T)dT= \gamma T^*l(lp)-l(lp)\int _{-\infty }^{T^*}p(T)dT\)

As a result, Expected Improvement for the Tree-structured Parzen Estimator can be expressed as:

Equation 13 proves that for the Expected Improvement to be maximized, points with high probability under \(l(lp)\) and low probability under \(g(lp)\) should be taken into consideration. Thus, on each iteration, the algorithm suggests a set of candidate launch points, \(lp^*\) , with the greatest Expected Improvement and, therefore, the minimum number of resulted turns in the designed paths.

5 Evaluation results

In this section, our proposed optimization scheme is evaluated via a simu-realistic pipeline exposing a number of UAV agents to a set of challenging CPP operations with i) the standard approach with pre-defined initial configurations and ii) the proposed methodology with optimal initial configurations while incorporating real-world geographic data and a high-fidelity simulator. For every experiment, the performance of each UAV is assessed towards a set of quality-of-flight (QoF) metrics such as the total energy consumption, the flight time, and the average distance traveled.

To enable robust simulations for autonomous UAVs and quantify their real-time performance, Air Learning (Krishnan et al. 2021 ), a high-fidelity open-source simulator, was utilized. Air Learning is a photo-realistic environment built on top of Unreal Engine 4 (UE4) with the usage of the AirSim (Shah et al. 2018 ) plugin to simulate the UAV’s physics and dynamics accurately. In the context of our benchmark comparison, all the experiments were carried out within Air Learning’s environment upon real-world geographic data by utilizing all the key features of our CPP methodology. The input coordinates of the polygon ROI and the initial UAV’s positions were transformed from the WGS84 to a local NED system (Cai et al. 2011 ) supported by Air Learning. We used real-world data to create a form of a real-time simulation to a degree indistinguishable from “true" reality that allows us to understand how the UAVs will respond in terms of QoF metrics without risking experiments on real robot platforms.

5.1 Quality of flight metrics

To quantify and assess the performance of the UAVs during the experiments, we considered the following Air Learning’s metrics:

Energy Consumed : The total energy, in kilojoules ( kJ ), spent per UAV during its mission. Energy consumption, based on UAV’s velocity and acceleration (Tseng et al. 2017 ; Boroujerdian et al. 2018 ), is estimated using the Columb counter method (Kumar et al. 2016 ), a technique used to track the capacity of a battery by measuring the active flowing current continuously over time to calculate the total sum of energy entering or leaving the battery pack.

Flight Time : The total flight time, in minutes ( min ), spent per UAV during its mission. Time is measured as the simulated run-time that orchestrates the overall mission execution.

Distance Traveled : The total distance, in meters ( m ), traveled per UAV during its mission. Distance is measured as the average length of the trajectory.

Using the aforementioned metrics, Air Learning allows us to continuously monitor the UAVs’ performance while operating over real-world geographic areas within the simulated world. It must be noted, that the QoF measurements for each UAV start and end at their initial positions.

5.2 Multi-UAV experimental setup

For the multi-UAV path planning experimental setup, AirSim’s UAV simulation model and the default \(simple\_flight\) controller were utilized. Two different scenarios were considered, with the first scenario comprising an obstacle-free area, while the second scenario involved three obstacles of arbitrary size and shape, located at random positions. In both scenarios, a predetermined polygon of approximately 170 acres was selected. The evaluation was performed by conducting ten experiments for each scenario, which were divided into two distinct groups called “Standard" and “Proposed". The first group consisted of five experiments utilizing pre-defined initial positions distributed at various locations within the polygon, while the second group involved five experiments using optimized initial positions for the UAVs. Each group of experiments was conducted with varying numbers of UAVs, including 3, 7, 11, 15, and 19, respectively, as depicted in Fig. 2 .

Resulting trajectories for each experimental scenario based on their initial positions represented by the red scatter points

Figure 2 presents a comparative analysis of the resulting trajectories of the mCPP for both experimental scenarios (with and without obstacles) based on the number of UAVs deployed during the trials. The first and third columns depict the outcomes when the UAVs’ initial positions were pre-defined, while the second and fourth columns exhibit the results when the UAVs’ positions were optimally placed at the beginning of each run based on the proposed optimization scheme. Upon comparing the performance of the CPP missions for each UAV, it becomes apparent that optimal initial positions exhibit superior alignment with the ROI while minimizing the total number of turns. Air Learning simulator, as depicted in Fig. 3 , was employed to launch the UAVs and execute the experiments, all flying at a fixed velocity of 5 m/s.

Air Learning simu-realistic environment supported by AirSim’s plugin

This comprehensive experimental design allowed us to investigate the impact of both pre-defined and optimal initial positions on the efficiency of the performance of each UAV in terms of QoF in challenging scenarios and evaluate the scalability by varying the number of UAVs.

5.2.1 Scenario #1: obstacle-free area

More specifically, regarding scenario #1, the diagrams depicted in Fig. 4 a, b, present the improved performance on the average Flight Time and Energy Consumption values as a function of the number of UAVs in the conducted trials. It is evident that regardless of the number of UAVs in each trial (x-axis), the performance of these two metrics in the case where the proposed approach has been applied consistently exceeds the performance of the standard approach. Additionally, as demonstrated by Fig. 4 c, our proposed optimization approach ensures that the resulting trajectories are modified in their shape rather than their length to align with the specific ROI. This feature is of paramount importance, as our optimization schema establishes the initial positions in a manner that preserves the length of the extracted CPP missions and their coverage capabilities, while simultaneously minimizing the mission execution time and energy consumption, ultimately leading to improved overall performance.

Average QoF metrics for the testing scenarios. The solid blue lines refer to the trials conducted using the standard approach, whereas the solid orange lines represent the experiments employing the proposed approach. In both scenarios, either in the obstacle-free area or in the obstacle area, the proposed approach leads to higher performance, implying a more accurate alignment of the UAV paths in the examined area

5.2.2 Scenario #2: obstacle area

Shifting to scenario #2, in the area featuring three obstacles possessing varied and irregular geometries and dimensions, the improved performance of the average Flight Time and Energy Consumption values as a function of the number of UAVs is depicted in Fig. 4 d and e. Similar to the first scenario, the performance of these two metrics consistently outperforms the standard approach across all experiments. Similarly to the previous case, Fig. 4 f representing the distance traveled by the drones illustrates that the proposed optimization method ensures that the resulting flight paths align with the corresponding regions, regardless of the arbitrary shape and size of the ROI.

Additional information regarding the distribution of the QoF metric for each UAV and their improved performance for both scenarios is provided in Appendix B.

6 Conclusions

The results obtained from the evaluation of our proposed optimization scheme demonstrate its ability to improve the quality of flight (QoF) metrics in challenging multi-UAV coverage path planning (mCPP) scenarios. We examined two distinct scenarios: one in an obstacle-free area and another in an area containing obstacles. In both cases, the optimization scheme achieved a notable enhancement in mission execution by aligning the trajectories with the region of interest (ROI) while minimizing the total number of turns.

In the obstacle-free scenario, the optimization scheme consistently improved the QoF metrics, resulting in reduced flight times, energy consumption, and distances traveled by the UAVs. The optimization scheme ensured that the UAVs’ trajectories adhered to the ROI’s shape, which is essential in scenarios like remote sensing and surveillance, where consistent data quality and resolution are crucial.

In the obstacle-containing scenario, the optimization scheme continued to demonstrate its effectiveness. The QoF metrics improved across the experiments, again leading to reduced flight times, energy consumption, and distances traveled. These results highlight the robustness and adaptability of the proposed optimization scheme in scenarios with complex obstacles.

By optimizing the initial launch points for UAVs in a multi-UAV coverage path planning problem, our proposed scheme maximizes the utilization of available resources while minimizing the mission execution time and energy consumption. This approach ensures that each UAV operates efficiently, avoids collisions with obstacles, and adheres to the no-fly zones, making it a valuable tool for real-world applications.

The effectiveness of the proposed optimization scheme lies in its ability to generate launch points that lead to paths with minimal turns, which is a key factor in minimizing mission time. By focusing on these controllable variables, we achieve efficient multi-UAV coverage path planning, even in the presence of obstacles or complex ROI shapes.

Overall, the results of the evaluation indicate that our proposed optimization scheme is a valuable tool for enhancing the performance of multi-UAV coverage path planning missions. It aligns trajectories with the ROI, reduces the number of turns, and improves QoF metrics, making it a promising approach for various real-world applications in surveillance, agriculture, and environmental monitoring, among others.

7 Future work

In our future research, we will aim to involve the dynamic adaptation of our optimization scheme to changing environments, taking into consideration real-time updates prompted by evolving obstacles or shifts in the region of interest (ROI). This entails developing algorithms capable of adjusting the optimization process to unexpected alterations in the operational landscape. Furthermore, our research will focus on practical implementation and validation of the adapted optimization scheme in real-world settings. This involves conducting extensive field tests to assess the scheme’s performance under diverse conditions, such as geographical landscapes and mission-specific requirements. The goal is to bridge the gap between theoretical advancements and practical applicability, ensuring that the optimization scheme remains effective and reliable in the dynamic and unpredictable environments where multi-UAV coverage path planning missions are conducted.

Data availability

For comprehensive access to our research materials, including the algorithm, evaluation mechanisms and results, please refer to the repositories at https://github.com/emmarapt/RealWorld2AirSim-DARP and https://github.com/alice-st/DARP_Optimal_Initial_Positions where all relevant data is available. These open-source repositories serve as a central hub for accessing and exploring the complete set of resources associated with our study.

The repositories can be found under https://github.com/emmarapt/RealWorld2AirSim-DARP and https://github.com/alice-st/DARP_Optimal_Initial_Positions

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Acknowledgements

This project has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme under grant agreement no 883302 (ISOLA).

Open access funding provided by HEAL-Link Greece. This project has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme under grant agreement no 883302 (ISOLA).

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Aliki Stefanopoulou, Emmanuel K. Raptis, Savvas D. Apostolidis, Socratis Gkelios, Athanasios Ch. Kapoutsis, Stefanos Vrochidis & Elias B. Kosmatopoulos

Department of Electrical and Computer Engineering, Democritus University of Thrace, Kimmeria Campus, Xanthi, 67100, Greece

Aliki Stefanopoulou, Emmanuel K. Raptis, Savvas D. Apostolidis, Socratis Gkelios & Elias B. Kosmatopoulos

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Aliki Stefanopoulou, Emmanuel K. Raptis, Savvas Apostolidis, Socratis Gkelios, Athanasios Ch. Kapoutsis contributed to the overall conceptualization, Methodology, Software development, data curation and original draft preparation. Savvas A. Chatzichristofis, Elias B. Kosmatopoulos, Stefanos Vrochidis performed supervision, resources and funding acquisition. All authors read and approved the final manuscript.

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This appendix shortly explains key components from other works, essential for comprehending the methodology presented in this work. Specifically, it explains aspects like the efficient representation of a ROI on grid, so that a grid-based CPP method can be applied efficiently to real-world operations (Apostolidis et al. 2022 , 2023 ), the division of the overall ROI to exclusive sub-regions, so that a region can be covered cooperatively by multiple unmanned vehicles (Kapoutsis et al. 2017 ), and finally some technical details about the STC algorithm (Gabriely and Rimon 2001 ), one of the most popular CPP approaches, that is used in all of the aforementioned and this newly introduced work.

The main objectives of the work presented in Apostolidis et al. ( 2022 ) is to deploy real-world multi-UAV coverage missions, following an approach that (i) ensures safe and efficient paths, (ii) respects the operational capabilities and motion limitations of the vehicles used, and (iii) ensures a high percentage of coverage for any shape and size of ROI, that may also include NGZs inside it. The methodology presented in Apostolidis et al. ( 2022 ) receives as input:

A user defined polygon ROI , of any shape, that may also include NGZs inside it, formatted in the WGS84 coordinate system (Cai et al. 2011 ).

The desired scanning density ( \(d_s\) ) for the mission, in meters, representing the distance between two sequential trajectories. This quantity is calculated automatically based on the the desired Ground Sampling Distance (GSD), determined by the sensor’s specifications and the flight’s altitude, and the desired sidelap between sequential images, a quantity that is usually dictated by the method that will later be used for data processing (Fig. 5 ). More information and formulas regarding the calculation of \(d_s\) can be found in both (Apostolidis et al. 2022 , 2023 ).

The number of UAVs, \(u_n\) , that will participate in the mission.

The initial positions of the UAVs in the operational area, real, user-defined, or random, formatted in WGS84 coordinates as well.

Scanning density explained

Given this input variables, the following steps take place:

Step 1: All coordinates are transformed to a local NED system (Cai et al. 2011 ), using a common reference point, to facilitate the calculations and transformations that will take place in the following steps.

Step 2: The ROI is represented on a grid, so that a grid-based mCPP method can be applied later on. For the representation of the ROI on grid, Apostolidis et al. ( 2022 ) uses a simplistic approach, where a check of whether the center of a grid’s cell is placed inside the polygon of the ROI, and outside of the defined NGZs, is performed. However, an optimization procedure for the calculation of an optimal for this purpose grid is introduced, that by rotating and shifting a grid over two axis manages to significantly improve the performance of the method in real-world operations (Fig. 6 ), as proved by extensive simulated evaluations. It should be noted that the discritization scale (size of the grids’ cells) is determined only by the user-defined \(d_s\) , since it directly corresponds to the density of the designed coverage trajectories in the real-world.

Node placement optimization procedure

Step 3: In this step, each of the grid’s cells acquires a status that can be "Free Space" , "Obstacle" , or "Initial Position" .

Step 4: Having a representation of the ROI on an optimal grid, along with the initial positions of the UAVs, DARP algorithm (Kapoutsis et al. 2017 ) undertakes to divide the overall region to sub-regions for each UAV to operate, ensuring the following criteria:

Generation of exclusive, spatially-connected regions for each UAV, to ensure collision free coverage operations.

Each UAV’s initial position is included inside its exclusive sub-region, eliminating redundant movements that do not contribute to the coverage procedure.

The union of all sub-regions reconstructs the initial ROI (as represented on the grid), ensuring complete coverage of all ROI’s cells.

The initial implementation (Kapoutsis et al. 2017 ) ensures equal sub-areas for each UAV, while a modification introduced in Apostolidis et al. ( 2022 ) allows for proportional areas’ allocation, according to the operational capabilities of each member of the group.

To achieve the area division and allocation, DARP performs a Voronoi partitioning, and iteratively builds a custom distance function, so that the area allocation fulfills the aforementioned criteria. Figure 7 depicts an example of area allocation during the execution of DARP, with Fig. 7 a showing the initial Voronoi partitions, Fig. 7 b showing the allocation during the execution of the algorithm, and Fig. 7 c showing the final, converged state.

Area allocation during different time-steps of DARP execution

Step 5: Once each unmanned vehicle is assigned with an exclusive sub-region to operate, for each of them a single-agent CPP problem is solved. In all the aforementioned works, STC algorithm is used for the trajectory generation, since the closed-loop paths allow the coverage procedure to start and end at any cell of the sub-region (facilitating to start the mission from the selected initial position, without compromising operational efficiency), and the guarantee of complete coverage and no-backtracking match the resource-efficient nature of these works. At a glance, STC uses two different grids for the trajectory generation, with the one having two times the discretization scale of the other. The center of the cells in the grid with the larger discretization scale are used as nodes to generate a MST, and the centers of the cells with the smaller discretization scale are used as waypoints for the generation of a trajectory that circumnavigates the MST. The result is a coverage trajectory for the given grid, that incorporates the features mentioned above. Figure 8 depicts the steps executed in STC to generate the coverage trajectory.

Spanning Tree Coverage algorithm explained

* While (Apostolidis et al. 2022 ) follows a more generic approach for the area representation on grid and a standard version of STC to generate trajectories, Apostolidis et al. ( 2023 ) applies certain modifications to Step 2 and Step 5 , taking into consideration the specificities of the path planning method that is applied, eliminating this way the discretization issues that are usually met when applying grid-based methods in real-world operations. To meet all possible requirements, it introduces three separate coverage modes - Geo-fenced Coverage Mode (GCM), Better Coverage Mode (BCM), and Complete Coverage Mode (CCM) - each of them incorporating features making them more appropriate for different types of real world-operations, but all of them eliminating the implementation issues met in Apostolidis et al. ( 2022 ). Figure 9 shows an example of coverage trajectories generated by Apostolidis et al. ( 2022 ) and Apostolidis et al. ( 2023 ) all coverage modes.

Coverage modes introduced in Apostolidis et al. ( 2023 )

Step 6: Having generated the coverage trajectories in the rotated and shifted grids, one for each member of the UAVs’ group, the inverse transformations are applied for the generated trajectories, to bring them back to the initial plane, and the NED coordinates are converted back to the WGS84 system to make them applicable in the real-world scenario.

Figure 10 depicts an example of a multi-UAV coverage mission generated by the core mCPP method used in this work. The method introduced in this work follows the pipeline described above, and specifically it performs equal area allocation, and is built upon (Apostolidis et al. 2023 ) GCM. This way, it inherits all of its features, making applicable and efficient for coverage operation even in very complex, non-convex ROIs that may include NGZs inside them, of any size. The main contribution of this work, however, is that instead of having real, user-defined, or random initial positions, that may lead to sub-regions of complex shapes, an optimization procedure is introduced, that by controlling the initial positions of the UAVs’ group forces DARP to generate sub-regions which lead to further increase of the operational efficiency, reducing the number of turns and overall operational duration, thus reducing the energy consumption of the UAVs as well.

Coverage example with the base methodology - in a non-convex polygon ROI - with NGZs - for 12 UAVs - with random initial positions

This appendix provides additional details on the application of our proposed optimization scheme for every UAV within a collaborative framework, as well as the percentage of improvement in the Quality of Flight metric values (QoF) for both scenarios. Figures 11 and 12 illustrate the distribution of QoF metric values for each UAV in the conducted trials, highlighting their performance in both scenarios. In both figures, the box plots use blue and orange colors to signify the standard and proposed approaches, with the percentage representing the improved performance in each experiment

Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverage of the obstacle-free area

Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverage of the obstacle area

As demonstrated by each comparative sub-box plot, the distribution of every QoF value is reduced when the optimization scheme is applied. Remarkably, the improved performance, represented as a percentage, associated with the Flight Time and Energy Consumption metrics is significantly high for every trial, implying that each UAV experienced favorable outcomes in terms of battery utilization, during its mission.

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Stefanopoulou, A., Raptis, E.K., Apostolidis, S.D. et al. Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions. Int J Intell Robot Appl (2024). https://doi.org/10.1007/s41315-024-00333-2

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The International Journal of Robotics Research: Sage Journals
International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research; it continues to supply scientists and students in robotics and related fields - artificial intelligence, applied mathematics, computer science, electrical and mechanical engineering - with timely, multidisciplinary material... This journal is peer-reviewed and is a member of the ...
The International Journal of Robotics Research
The International Journal of Robotics Research. A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research. IJRR offers incisive and thought-provoking original research papers and articles, perceptive reviews, and lively ...
Journal Description: The International Journal of Robotics Research
A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research. IJRR offers incisive and thought-provoking original research papers and articles, perceptive reviews, and lively editorials on ground-breaking trends issues ...
International Journal of Robotics Research
International Journal of Robotics Research Review Speed, Peer-Review Duration, Revision Process, Time from Submission to 1st Editorial/Reviewer Decision & Time from Submission to Acceptance/Publication. ... Based on the International Journal of Robotics Research Review Speed Feedback System, it takes authors - days to get the first editorial ...
The International Journal of Robotics Research
Res. The International Journal of Robotics Research is a peer-reviewed scientific journal that covers the field of robotics on topics from sensors and sensory interpretations to kinematics in motion planning. Its editor-in-chief is Antonio Bicchi ( University of Pisa ). The journal was established in 1982 and is published by SAGE Publications .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Home
International Journal of Robotics Research. Sage Publications, Inc. 2455 Teller Road Thousand Oaks, CA. United States. Get Alerts for this Periodical.
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A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research. IJRR offers incisive and thought-provoking original research papers and articles, perceptive reviews, and lively editorials on ground-breaking trends issues ...
International Journal of Robotics Research
Scope. It is the policy of The International Journal of Robotics Research to encourage the application of theoretical advances to real problems and data. Results should represent a significant rather than incremental advance, and should be verified appropriately according to the topic. Experimental results are strongly encouraged.
The international journal of robotics research (Online)
Preserved. SAGE Publications. National Library of the Netherlands. 1982. 2019. 20/10/2023
Design and Kinematic Modeling of Constant Curvature Continuum Robots: A
Nonholonomic modeling of needle steering. The International Journal of Robotics Research, 25(5/6): 509-526. Google Scholar Digital Library; Webster, R.J., III, Memisevic, J. and Okamura, A.M. ( 2005). Design considerations for robotic needle steering . IEEE International Conference on Robotics and Automation, pp. 3599-3605. Google Scholar
International Journal of Robotics Research
The Impact IF 2022 of International Journal of Robotics Research is 10.56, which is computed in 2023 as per its definition. International Journal of Robotics Research IF is increased by a factor of 2.41 and approximate percentage change is 29.57% when compared to preceding year 2021, which shows a rising trend.
The International Journal of Robotics Research
The International Journal of Robotics Research: Create email alert. Also from Sage. CQ Library Elevating debate opens in new tab; Sage Data Uncovering insight opens in new tab; Sage Business Cases Shaping futures opens in new tab; Sage Campus Unleashing potential opens in new tab;
Industrial Robot
The international journal of robotics research and application. Subjects: RSS feed. Search. All issues; EarlyCite; Volume 51 . Issue 2 2024. Issue 1 2024. Volume 50 . Issue 6 2023. ... Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for ...
Foundation Models and Neural-Symbolic AI for Robotics
The field of robotics has witnessed unprecedented growth in the integration of machine learning models, especially with the recent advent of foundation models, neural-symbolic AI, generative models (large language models, diffusion models, etc), and other emerging learning models.These methods have revolutionized various tasks in computer vision, natural language processing, computer graphics ...
A Literature Review on New Robotics: Automation from Love to War
This article investigates the social significance of robotics for the years to come in Europe and the US by studying robotics developments in five different areas: the home, health care, traffic, the police force, and the army. Our society accepts the use of robots to perform dull, dangerous, and dirty industrial jobs. But now that robotics is moving out of the factory, the relevant question ...
Robotics
Robotics is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI.The IFToMM is affiliated with Robotics and its members receive a discount on the article processing charges.. Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.; High Visibility: indexed within Scopus, ESCI (Web of Science ...
Submission Guidelines: The International Journal of Robotics Research
Before submitting your manuscript to The International Journal of Robotics Research (IJRR), please ensure you have read the journal's Aims & Scope. 1.2 Article Types. The International Journal of Robotics Research publishes. Original articles of archival value, produced to advance science and technology in the field, and stay valuable in time.
International Journal of Robotics Research
The acceptance rate for an academic journal is dependent upon the relative demand for publishing in a particular journal, the peer review processes in place, the mix of invited and unsolicited submissions, and time to publication, among others. ... The International Journal of Robotics Research is a peer-reviewed scientific journal that covers ...
Improving time and energy efficiency in multi-UAV coverage ...
This paper focuses on Coverage Path Planning (CPP) methodologies, particularly in the context of multi-robot missions, to efficiently cover user-defined Regions of Interest (ROIs) using groups of UAVs, while emphasizing on the reduction of energy consumption and mission duration. Optimizing the efficiency of multi-robot CPP missions involves addressing critical factors such as path length, the ...
The International Journal of Robotics Research
The International Journal of Robotics Research - Volume 43, Number 1, Jan 01, 2024. The International Journal of Robotics Research. Impact Factor: 9.2 / 5-Year Impact Factor: 9.6. JOURNAL HOMEPAGE.
[1905.06113] Human Motion Trajectory Prediction: A Survey
Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities ...
Optimal virtual tube planning and control for swarm robotics
This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm robotics in cluttered environments, but optimizing trajectories for each robot is still computationally expensive, with a computational complexity from O ( k ...
The International Journal of Robotics Research
The International Journal of Robotics Research. Impact Factor: 9.2 / 5-Year Impact Factor: 9.6 . JOURNAL HOMEPAGE. SUBMIT PAPER. Previous issue. Next issue. Volume 40 Issue 1, January 2021. Special Issue on Soft Robotic Modeling and Control: Bringing Together Articulated Soft Robots and Soft-Bodied Robots.
Emerging Technologies for Automation in Environmental Sensing: Review
This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive ...
The International Journal of Robotics Research
A leading peer-reviewed journal in its field for more than two decades, The International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research. IJRR offers incisive and thought-provoking original research papers and articles, perceptive reviews, and lively editorials on ground-breaking trends issues ...
The International Journal of Robotics Research
Claire Dune. Maxime Ferrera. [...] View all. Preview abstract. xml GET ACCESS. Table of contents for The International Journal of Robotics Research, 42, 9, Aug 01, 2023.
Quantitative analysis of transfer and incremental learning for image
International Journal of Computational Vision and Robotics Volume 14 Issue 2 2024 pp 202-212 https: ... systematic review, models, challenges, and research directions. ... International Journal of Computational Vision and Robotics Volume 14, Issue 2. 2024. 119 pages. ISSN: 1752-9131.

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Industrial Robot

The role of robots in logistics

Prototyping of compliant grippers using FFF and TPU

Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments

Online path planning of pork cutting robot using 3D laser point cloud

LIDAR-based SLAM system for autonomous vehicles in degraded point cloud scenarios: dynamic obstacle removal

Localization of asparagus spears using time-of-flight imaging for robotic harvesting

Attitude oscillation suppression control of a XK-I spherical robot

Impedance control for force tracking of a dual-arm cooperative robot based on particle swarm optimization

SiLK-SLAM: accurate, robust and versatile visual SLAM with simple learned keypoints

Design and experiment of transmission tower climbing robot inspired by inchworm

Calibration strategies for enhancing accuracy in serial industrial robots for orbital milling applications

Development of a novel nonrigid support friction stir welding repair robot for aluminum alloy train

DARLOS: a lightweight dual-arm robotic live-line operation system for autonomous high-voltage distribution grid maintenance

A robot motion skills method with explicit environmental constraints

An efficient constraint method for solving planning problems under end-effector constraints

Piston-like particle jamming for enhanced stiffness adjustment of soft robotic arm

Fuzzy logic system-based force tracking control of robot in highly dynamic environments

Research on dual robot collaboration method based on improved double ant colony algorithm

Narrow gap welding seam deflection correction study based on passive vision

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

A heavy-load wall-climbing robot for bridge concrete structures inspection

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

An efficient and accurate force/torque sensing method based on an excitation trajectory

Operation space analysis and trajectory planning of mechanical arm in narrow space for GIS (gas insulated switchgear) inspection robot

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Foundation Models and Neural-Symbolic AI for Robotics

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Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions

Cite this article

1 Introduction

1.1 Contributions

1.2 Paper outline

2 Related work

3 Problem formulation

3.1 ROI representation on grid, task allocation and path planning

3.2 Path execution

3.3 Evaluation metrics

3.4 Path time calculation

3.5 Minimizing mission time

3.6 Controllable variables

3.7 Decision variables

3.8 Operational constraints

3.9 Optimization problem

4 Methodology

4.1 Selection function

5 Evaluation results

5.1 Quality of flight metrics

5.2 Multi-UAV experimental setup

5.2.1 Scenario #1: obstacle-free area

5.2.2 Scenario #2: obstacle area

6 Conclusions

7 Future work

Data availability

Acknowledgements

Author information

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