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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: 2020-05-07Bibliographically 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
2. Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents
Open this publication in new window or tab >>Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Research on autonomous agents and vehicles has gained momentum in the past years, which is reflected in the extensive body of literature and the investment of big players of the industry in the development of products such as self-driving cars. Additionally, these systems are envisioned to continuously communicate and cooperate with one another in order to adapt to dynamic circumstances and unforeseeable events, and as a result will they fulfil their goals even more efficiently.The facilitation of such dynamic collaboration and the modelling of interactions between different actors (software agents, humans) remains an open challenge.This thesis tackles the problem of enabling dynamic collaboration by investigating the automated adjustment of autonomy of different agents, called Adaptive Autonomy (AA). An agent, in this context, is a software able to process and react to sensory inputs in the environment in which it is situated in, and is additionally capable of autonomous actions. In this work, the collaborative adaptive autonomous behaviour of agents is shaped by their willingness to interact with other agents, that captures the disposition of an agent to give and ask for help, based on different factors that represent the agent's state and its interests.The AA approach to collaboration is used in two different domains: (i) the hunting mobile search problem, and (ii) the coverage problem of mobile wireless sensor networks. In both cases, the proposed approach is compared to state-of-art methods.Furthermore, the thesis contributes on a conceptual level by combining and integrating the AA approach -- which is purely distributed -- with a high-level mission planner, in order to exploit the ability of dealing with local and contingent problems through the AA approach, while minimising the requests for a re-plan to the mission planner.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 314
National Category
Engineering and Technology Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47905 (URN)978-91-7485-468-8 (ISBN)
Public defence
2020-06-12, Västerås Campus (+ Online/Zoom), Mälardalens högskola, Västerås, 10:00 (English)
Opponent
Supervisors
Available from: 2020-05-08 Created: 2020-05-06 Last updated: 2020-05-13Bibliographically approved

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Frasheri, MirgitaCuruklu, BaranEkström, MikaelPapadopoulos, Alessandro

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