AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms

Dhaminda B. Abeywickrama*, James Wilson, Suet Lee, Greg Chance, Peter D. Winter, Arianna Manzini, Ibrahim Habli, Shane Windsor, Sabine Hauert, Kerstin Eder

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The behaviours of a swarm are not explicitly engineered. Instead, they are an emergent consequence of the interactions of individual agents with each other and their environment. This emergent functionality poses a challenge to safety assurance. The main contribution of this paper is a process for the safety assurance of emergent behaviour in autonomous robotic swarms called AERoS, following the guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). We explore our proposed process using a case study centred on a robot swarm operating a public cloakroom.

Original languageEnglish
Title of host publicationComputer Safety, Reliability, and Security. SAFECOMP 2023 Workshops - ASSURE, DECSoS, SASSUR, SENSEI, SRToITS, and WAISE, Proceedings
EditorsJérémie Guiochet, Stefano Tonetta, Erwin Schoitsch, Matthieu Roy, Friedemann Bitsch
PublisherSpringer Science and Business Media Deutschland GmbH
Pages341-354
Number of pages14
ISBN (Electronic)9783031409530
ISBN (Print)9783031409523
DOIs
Publication statusPublished - 14 Sept 2023
EventInternational Conference on Computer Safety, Reliability, and Security, SAFECOMP 2023 - Toulouse, France
Duration: 19 Sept 202322 Sept 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computer Safety, Reliability, and Security, SAFECOMP 2023
Country/TerritoryFrance
CityToulouse
Period19/09/2322/09/23

Bibliographical note

Funding Information:
Acknowledgements. This workshop is partially funded by ERC Consolidator grant 864075 CAESAR.

Funding Information:
This work was partially supported by AISEC Project EP/T027037/1.

Funding Information:
Acknowledgements. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organisations (see https:// www.grid5000.fr). This work has been partially supported by MIAI@Grenoble Alpes, (ANR-19-P3IA-0003) and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.

Funding Information:
Acknowledgements. Part of the work presented in the workshop received funding from the EC (H2020/ECSEL Joint Undertaking) and the partners National Funding Authorities (“tri-partite”) through the projects SECREDAS (nr. 783119), Comp4Drones (nr. 826610), AI4CSM (nr. 101007326). The AIMS5.0 project. Funded by the HORIZON-KDT-JU-2022-1-IA, project no. 101112089 and national funding authorities of the partners. The project ADEX is funded by the national Austrian Research Promotion Agency FFG in the program “ICT for Future” (FFG, BMK Austria) (no. 880811). The TEACHING project is funded by the EU Horizon 2020 research and innovation programme under GA n.871385, the LABYRINTH2020 project was funded under GA 861696. This list does not claim to be complete, for further details check the papers.

Funding Information:
Acknowledgments. Distribution statement “A” (approved for public release, distribution unlimited). This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), contract FA875020C0508. The views, opinions, or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. The authors wish to also acknowledge the partial support by the National Science Foundation (NSF) under Awards 1846524 and 2139982, the Office of Naval Research (ONR) under Award N00014-20-1-2258, the Defense Advanced Research Projects Agency (DARPA) under Award HR00112010003, and the Okawa Research Grant.

Funding Information:
Acknowledgments. The research described in this paper has been supported by the project MAIA “Monitoraggio Attivo dell’Infrastruttura” funded by MIUR PON 14-20 (id code ARS01_00353).

Funding Information:
Acknowledgments. This study was sponsored by the National Key R&D Program of China (2020YFB1600400).

Funding Information:
Supported by the H2020 project TEACHING (n. 871385) - www.teaching-h2020.eu.

Funding Information:
The authors would like to thank Alvin Wilby, John Downer, Jonathan Ives, and the AMLAS team for their fruitful comments. The work presented has been supported by the UKRI Trustworthy Autonomous Systems Node in Functionality under Grant EP/V026518/1. I.H. is supported by the Assuring Autonomy International Programme at the University of York.

Funding Information:
Acknowledgments. This work has been supported by the French government under the “France 2030” program, as part of the SystemX Technological Research Institute. The AIMOS tool is also funded under the Horizon Europe TRUMPET project grant no. 101070038 and the European Defence Fund AINCEPTION project grant no. 101103385.

Funding Information:
Acknowledgements. We are grateful to the SAFECOMP organization committee and collaborators for their support in arranging SASSUR, especially to Erwin Schoitsch and Matthieu Roy as Workshop Chairs and to Friedemann Bitsch as Publication Chair. We also thank all the authors of the submitted papers for their interest in the workshop, and the program committee for its work. Finally, the workshop is supported by the 4DASafeOps (Sweden’s Software Center), ET4CQPPAJ (Sweden’s Software Center), iRel4.0 (H2020-ECSEL ref. 876659; MCIN/AEI ref. PCI2020-112240; NextGen.EU/PRTR), REBECCA (HORIZON-KDT ref. 101097224; MCIN/AEI ref. PCI2022-135043-2; NextGen.EU/PRTR), VALU3S (H2020-ECSEL ref. 876852; MCIN/AEI ref. PCI2020-112001; NextGen.EU/ PRTR), and ETHEREAL (MCIN/AEI ref. PID2020-115220RB-C21; ERDF) projects, and by the Ramon y Cajal Program (MCIN/AEI ref. RYC-2017-22836; ESF).

Funding Information:
Supported by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project) (2022JBXT003).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Assurance
  • Emergent behaviour
  • Guidance
  • Safety
  • Swarms

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