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

Mickey H L Li; Arthur G Richards; Mahesh Sooriyabandara

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 proceeding › Conference 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 language	English
Title of host publication	AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
Publisher	Association for Computing Machinery (ACM)
Pages	1584-1586
Number of pages	3
ISBN (Electronic)	9781450383073
ISBN (Print)	9781450383073
Publication status	Published - 19 Apr 2021

Keywords

Multi-Robot Systems
Coverage Path Planning
Reliability Analysis
Genetic Algorithm

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1 Conference Paper

Asynchronous Reliability-Aware Multi-UAV Coverage Path Planning
Li, M. H. L., Richards, A. G. & Sooriyabandara, M., 18 Oct 2021, p. 10023. 10029 p.
Research output: Contribution to conference › Conference Paper › peer-review

Open Access
8 Citations (Scopus)

Cite this

@inproceedings{d88832f9de5a465e93c481e20faa4af2,

title = "Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm",

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{\textquoteright}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.",

keywords = "Multi-Robot Systems, Coverage Path Planning, Reliability Analysis, Genetic Algorithm",

author = "Li, {Mickey H L} and Richards, {Arthur G} and Mahesh Sooriyabandara",

year = "2021",

month = apr,

day = "19",

language = "English",

isbn = "9781450383073",

pages = "1584--1586",

booktitle = "AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

}

Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm. / Li, Mickey H L; Richards, Arthur G; Sooriyabandara, Mahesh.
AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. Association for Computing Machinery (ACM), 2021. p. 1584-1586.

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

TY - GEN

T1 - Reliability-Aware Multi-UAV Coverage Path Planning using a Genetic Algorithm

AU - Li, Mickey H L

AU - Richards, Arthur G

AU - Sooriyabandara, Mahesh

PY - 2021/4/19

Y1 - 2021/4/19

N2 - 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.

AB - 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.

KW - Multi-Robot Systems

KW - Coverage Path Planning

KW - Reliability Analysis

KW - Genetic Algorithm

M3 - Conference Contribution (Conference Proceeding)

SN - 9781450383073

SP - 1584

EP - 1586

BT - AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems

PB - Association for Computing Machinery (ACM)

ER -

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

Abstract

Keywords

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Research output

Asynchronous Reliability-Aware Multi-UAV Coverage Path Planning

Cite this