A Decentralized Asynchronous Collaborative Genetic Algorithm for Heterogeneous Multi-agent Search and Rescue Problems

In this paper we propose a version of the Genetic Algorithm (GA) for combined task assignment and path planning that is highly decentralized in the sense that each agent only knows its own capabilities and data, and a set of so-called handover values communicated to it from the other agents over an...

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Vydáno v:IEEE International Symposium on Safety, Security and Rescue Robotics s. 1 - 8
Hlavní autoři: Pallin, Martin, Rashid, Jayedur, Ogren, Petter
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 25.10.2021
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ISSN:2475-8426
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Abstract In this paper we propose a version of the Genetic Algorithm (GA) for combined task assignment and path planning that is highly decentralized in the sense that each agent only knows its own capabilities and data, and a set of so-called handover values communicated to it from the other agents over an unreliable low bandwidth communication channel. These handover values are used in combination with a local GA involving no other agents, to decide what tasks to execute, and what tasks to leave to others. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication.
AbstractList In this paper we propose a version of the Genetic Algorithm (GA) for combined task assignment and path planning that is highly decentralized in the sense that each agent only knows its own capabilities and data, and a set of so-called handover values communicated to it from the other agents over an unreliable low bandwidth communication channel. These handover values are used in combination with a local GA involving no other agents, to decide what tasks to execute, and what tasks to leave to others. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare the performance of our approach to a centralized version of GA, and a partly decentralized version of GA where computations are local, but all agents need complete information regarding all other agents, including position, range, battery, and local obstacle maps. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication. We compare solution performance as well as messages sent for the three algorithms, and conclude that the proposed algorithms has a small decrease in performance, but a significant decrease in required communication.
Author Pallin, Martin
Rashid, Jayedur
Ogren, Petter
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  givenname: Martin
  surname: Pallin
  fullname: Pallin, Martin
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  organization: KTH - Royal Institute of Technology,Saab division of Command and Control systems,Stockhlm,Sweden
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  givenname: Jayedur
  surname: Rashid
  fullname: Rashid, Jayedur
  email: jayedur.rashid@saabgroup.com
  organization: KTH - Royal Institute of Technology,Saab division of Command and Control systems,Stockhlm,Sweden
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  givenname: Petter
  surname: Ogren
  fullname: Ogren, Petter
  organization: KTH - Royal Institute of Technology,Division of Robotics, Perception and Learning,Stockhlm,Sweden
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Snippet In this paper we propose a version of the Genetic Algorithm (GA) for combined task assignment and path planning that is highly decentralized in the sense that...
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SubjectTerms Batteries
Communication channels
Handover
Path planning
Safety
Search problems
Security
Title A Decentralized Asynchronous Collaborative Genetic Algorithm for Heterogeneous Multi-agent Search and Rescue Problems
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