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GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
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: 0000-0002-9051-929X
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
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2020 (English)Report (Other academic)
Abstract [en]

Multi-robot systems can be prone to failures during plan execution, depending on the harshness of the environment they are deployed in. As a consequence, initially devised plans may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. Two main approaches emerge as possible solutions, a global re-planning technique using a centralized planner that will redo the task allocation with the updated world state information, or a decentralized approach that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots.The former approach produces an overall better solution, while the latter is less computationally expensive.The goal of this paper is to exploit the benefits of both approaches, while minimizing their drawbacks. To this end, we propose a hybrid approach {that combines a centralized planner with decentralized multi-agent planning}. In case of an agent failure, the local plan reparation algorithm tries to repair the plan through agent negotiation. If it fails to re-allocate all of the pending tasks, the global re-planning algorithm is invoked, which re-allocates all unfinished tasks from all agents.The hybrid approach was compared to planner approach, and it was shown that it improves on the makespan of a mission in presence of different numbers of failures,as a consequence of the local plan reparation algorithm.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Multi-Agent Systems, Autonomous Agents, Centralized Planning, Decentralized Planning
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-47902OAI: oai:DiVA.org:mdh-47902DiVA, id: diva2:1428869
Available from: 2020-05-06 Created: 2020-05-06 Last updated: 2022-11-08Bibliographically approved
In thesis
1. 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: 2022-11-08Bibliographically approved

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Frasheri, MirgitaMiloradović, BrankoCuruklu, BaranEkström, MikaelPapadopoulos, Alessandro

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