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Adaptive Autonomy in a Search and Rescue Scenario
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Robotics)ORCID iD: 0000-0001-7852-4582
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Robotics)ORCID iD: 0000-0002-5224-8302
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Robotics)ORCID iD: 0000-0002-5832-5452
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1364-8127
2018 (English)In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019, 2018, p. 150-155Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy. 

Place, publisher, year, edition, pages
2018. p. 150-155
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-40254DOI: 10.1109/SASO.2018.00026ISI: 000459885200016Scopus ID: 2-s2.0-85061910844ISBN: 9781538651728 (print)OAI: oai:DiVA.org:mdh-40254DiVA, id: diva2:1233552
Conference
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2019-03-14Bibliographically approved
In thesis
1. Collaborative Adaptive Autonomous Agents
Open this publication in new window or tab >>Collaborative Adaptive Autonomous Agents
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Mälardalen University Press, 2018
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 271
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40255 (URN)978-91-7485-399-5 (ISBN)
Presentation
2018-09-14, Gamma, Mälardalens högskola, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2018-07-19 Created: 2018-07-18 Last updated: 2019-10-01Bibliographically approved

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Frasheri, MirgitaCuruklu, BaranEkström, MikaelPapadopoulos, Alessandro

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CiteExportLink to record
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Citation style
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