Reducing Efficiency Degradation Due to Scheduled Agent Suspensions by Task Handover in Multi-Agent Cooperative Patrol Problems

Authors

  • Sota Tsuiki Waseda University
  • Keisuke Yoneda Waseda University
  • Toshiharu Sugawara Waseda University

DOI:

https://doi.org/10.32473/flairs.v34i1.128442

Keywords:

planned suspension, multi-agent cooperative patrol problem, negotiation, task delegation, security patrol, periodic inspection, maintenance

Abstract

This paper proposes a method to mitigate the significant performance degradation due to planned suspensions in the multi-agent cooperative patrol problem. In recent years, there has been an increased demand to utilize multiple intelligent agents that control robots. Furthermore, cooperation between multiple agents is required for performing tasks that are complex and/or cover large spaces. However, since robots are machines, they must be periodically inspected or replaced with new ones to prevent unintended breakdowns for continuous operation and to prolong the lifetime of agents as much as possible. However, such suspension of agents for inspection can cause a sudden deterioration in performance, which is not ignorable in some applications. Meanwhile, such suspensions are usually planned; thus, we can know in advance which agents will stop, and when, to anticipate a preparation period before the actual suspension time. Thus, we introduce a negotiation method in which the agents that are scheduled to be suspended hand over some responsible and important tasks to other agents to reduce the impact of a sudden performance degradation. The experimental results show that the proposed method considerably reduces the performance degradation, especially for security patrol applications.

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Published

2021-04-18

How to Cite

Tsuiki, S., Yoneda, K., & Sugawara, T. (2021). Reducing Efficiency Degradation Due to Scheduled Agent Suspensions by Task Handover in Multi-Agent Cooperative Patrol Problems. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128442

Issue

Section

Special Track: Autonomous Robots and Agents