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Modelling and Control of the Collaborative Behavior of Adaptive Autonomous Agents
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Robotics)ORCID-id: 0000-0001-7852-4582
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: urn:nbn:se:mdh:diva-47905ISBN: 978-91-7485-468-8 (tryckt)OAI: oai:DiVA.org:mdh-47905DiVA, id: diva2:1428873
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
Delarbeid
1. TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model
Åpne denne publikasjonen i ny fane eller vindu >>TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model
Vise andre…
2019 (engelsk)Inngår i: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, s. 478-486Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-46316 (URN)10.1007/978-3-030-33792-6_32 (DOI)000587904300032 ()2-s2.0-85076411190 (Scopus ID)978-3-030-33791-9 (ISBN)
Konferanse
The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and ControlUnicorn -Sustainable, peaceful and efficient robotic refuse handlingAggregate Farming in the Cloud
Tilgjengelig fra: 2019-12-12 Laget: 2019-12-12 Sist oppdatert: 2021-11-22bibliografisk kontrollert
2. Adaptive Autonomy in a Search and Rescue Scenario
Åpne denne publikasjonen i ny fane eller vindu >>Adaptive Autonomy in a Search and Rescue Scenario
2018 (engelsk)Inngår i: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019, 2018, s. 150-155Konferansepaper, Publicerat paper (Fagfellevurdert)
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. 

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-40254 (URN)10.1109/SASO.2018.00026 (DOI)000459885200016 ()2-s2.0-85061910844 (Scopus ID)9781538651728 (ISBN)
Konferanse
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018
Tilgjengelig fra: 2018-07-18 Laget: 2018-07-18 Sist oppdatert: 2020-05-07bibliografisk kontrollert
3. GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
Åpne denne publikasjonen i ny fane eller vindu >>GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
Vise andre…
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.

Emneord
Multi-Agent Systems, Autonomous Agents, Centralized Planning, Decentralized Planning
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-47902 (URN)
Tilgjengelig fra: 2020-05-06 Laget: 2020-05-06 Sist oppdatert: 2022-11-08bibliografisk kontrollert
4. Modeling the willingness to interact in cooperative multi-robot systems
Åpne denne publikasjonen i ny fane eller vindu >>Modeling the willingness to interact in cooperative multi-robot systems
2020 (engelsk)Inngår i: ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence, VOL 1, SciTePress , 2020, Vol. 1, s. 62-72Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

When multiple robots are required to collaborate in order to accomplish a specific task, they need to be coordinated in order to operate efficiently. To allow for scalability and robustness, we propose a novel distributed approach performed by autonomous robots based on their willingness to interact with each other. This willingness, based on their individual state, is used to inform a decision process of whether or not to interact with other robots within the environment. We study this new mechanism to form coalitions in the on-line multiobject κ-coverage problem, and compare it with six other methods from the literature. We investigate the trade-off between the number of robots available and the number of potential targets in the environment. We show that the proposed method is able to provide comparable performance to the best method in the case of static targets, and to achieve a higher level of coverage with respect to the other methods in the case of mobile targets.

sted, utgiver, år, opplag, sider
SciTePress, 2020
Emneord
Adaptive Autonomy, Robot Collaboration, κ-Coverage Problem
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-47559 (URN)10.5220/0008951900620072 (DOI)000570767700005 ()2-s2.0-85083267753 (Scopus ID)9789897583957 (ISBN)
Konferanse
12th International Conference on Agents and Artificial Intelligence, ICAART 2020; Valletta; Malta; 22 February 2020 through 24 February 2020; Code 158715
Tilgjengelig fra: 2020-04-23 Laget: 2020-04-23 Sist oppdatert: 2022-11-08bibliografisk kontrollert
5. Adaptive Autonomy in Wireless Sensor Networks
Åpne denne publikasjonen i ny fane eller vindu >>Adaptive Autonomy in Wireless Sensor Networks
Vise andre…
2020 (engelsk)Inngår i: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2020, s. 375-383Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Moving nodes in a Mobile Wireless Sensor Network (MWSN) typically have two maintenance objectives: (i) extend the coverage of the network as long as possible to a target area, and (ii) extend the longevity of the network as much as possible. As nodes move and also route traffic in the network, their battery levels deplete differently for each node. Dead nodes lead to loss of connectivity and even to disengaging full parts of the network. Several reactive and rule-based approaches have been proposed to solve this issue by adapting redeployment to depleted nodes. However, in large networks a cooperative approach may increase performance by taking the evolution of node battery and traffic into account. In this paper, we present a hybrid agent-based architecture that addresses the problem of depleting nodes during the maintenance phase of a MWSN. Agents, each assigned to a node, collaborate and adapt their behaviour to their battery levels. The collaborative behavior is modeled through the willingness to interact abstraction, which defines when agents ask and give help to one another. Thus, depleting nodes may ask to be replaced by healthier counterparts and move to areas with less traffic or to a collection point. At the lower level, negotiations trigger a reactive navigation behaviour based on Social Potential Fields (SPF). It is shown that the proposed method improves coverage and extends network longevity in an environment without obstacles as compared to SPF alone.

Emneord
self-organisation; multi-robot systems; networked systems and dis-tributed robotics
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-47904 (URN)2-s2.0-85096684468 (Scopus ID)9781450375184 (ISBN)
Konferanse
19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'20)
Tilgjengelig fra: 2020-05-06 Laget: 2020-05-06 Sist oppdatert: 2022-11-08bibliografisk kontrollert

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