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GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Robotics)ORCID-id: 0000-0001-7852-4582
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Robotics)ORCID-id: 0000-0002-9051-929X
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Robotics)ORCID-id: 0000-0002-5224-8302
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Robotics)ORCID-id: 0000-0002-5832-5452
Vise andre og tillknytning
2020 (engelsk)Rapport (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
2020.
Emneord [en]
Multi-Agent Systems, Autonomous Agents, Centralized Planning, Decentralized Planning
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-47902OAI: oai:DiVA.org:mdh-47902DiVA, id: diva2:1428869
Tilgjengelig fra: 2020-05-06 Laget: 2020-05-06 Sist oppdatert: 2022-11-08bibliografisk kontrollert
Inngår i avhandling
1. Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents
Åpne denne publikasjonen i ny fane eller vindu >>Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2020
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 314
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-47905 (URN)978-91-7485-468-8 (ISBN)
Disputas
2020-06-12, Västerås Campus (+ Online/Zoom), Mälardalens högskola, Västerås, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2020-05-08 Laget: 2020-05-06 Sist oppdatert: 2022-11-08bibliografisk kontrollert

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Frasheri, MirgitaMiloradović, BrankoCuruklu, BaranEkström, MikaelPapadopoulos, Alessandro

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