Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm

Mickey H L Li, Arthur G Richards, Mahesh Sooriyabandara

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Graceful degradation is a desirable trait in applications that require coverage with real, failure-prone robots. This paper uses methods informed by Reliability Engineering to study the Reliability-Aware Multi-Agent Coverage Path Planning (RA-MCPP) problem. An augmented stochastic framework is applied to evaluate a strategy’s probability of mission completion (PoC) on 3D lattice graph environments. A Genetic Algorithm optimisation approach is then proposed to find RA-MCPP path plans which maximise PoC. It is shown that the GA provides good solutions at reasonable runtimes, complementing previous approaches which focused on global optimality guarantees at the cost of massive computation, especially for medium and large environments.
Original languageEnglish
Title of host publicationAAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
PublisherAssociation for Computing Machinery (ACM)
Pages1584-1586
Number of pages3
ISBN (Electronic)978-1-4503-8307-3
ISBN (Print)978-1-4503-8307-3
Publication statusPublished - 19 Apr 2021

Keywords

  • Multi-Robot Systems
  • Coverage Path Planning
  • Reliability Analysis
  • Genetic Algorithm

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