Multiregional Coverage Path Planning for Multiple Energy Constrained UAVs

In recent years, we have witnessed a growing use of unmanned aerial vehicles (UAVs) in a variety of civil, commercial and military applications. Among these applications, many require the UAVs to scan or survey one or more regions, such as land monitoring, disaster assessment, search and rescue. To...

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Vydané v:	IEEE transactions on intelligent transportation systems Ročník 23; číslo 10; s. 17366 - 17381
Hlavní autori:	Xie, Junfei, Chen, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation algorithms Autonomous aerial vehicles branch and bound Constraints Coverage path planning genetic algorithm Genetic algorithms Military applications Monitoring multiple regions multiple unmanned aerial vehicles Optimization Path planning Robots Task analysis Traveling salesman problem Unmanned aerial vehicles
ISSN:	1524-9050, 1558-0016
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Abstract	In recent years, we have witnessed a growing use of unmanned aerial vehicles (UAVs) in a variety of civil, commercial and military applications. Among these applications, many require the UAVs to scan or survey one or more regions, such as land monitoring, disaster assessment, search and rescue. To realize such applications, path planning is a key step. Although the coverage path planning (CPP) problem for a single region has been extensively studied in the literature, CPP for multiple regions has gained much less attention. This multi-regional CPP problem can be considered as a variant of the (multiple) traveling salesman problem (TSP) enhanced with CPP. Previously, we have studied the case of a single UAV. In this paper, we extend our previous studies to further consider multiple UAVs with energy constraints. To solve this new path planning problem, we develop two approaches: 1) a branch-and-bound (BnB) based approach that can find (near) optimal tours and 2) a genetic algorithm (GA) based approach that can solve large-scale problems efficiently under different objectives. Comprehensive theoretical analyses and computational experiments demonstrate the promising performance of the proposed approaches in terms of optimality and efficiency.
AbstractList	In recent years, we have witnessed a growing use of unmanned aerial vehicles (UAVs) in a variety of civil, commercial and military applications. Among these applications, many require the UAVs to scan or survey one or more regions, such as land monitoring, disaster assessment, search and rescue. To realize such applications, path planning is a key step. Although the coverage path planning (CPP) problem for a single region has been extensively studied in the literature, CPP for multiple regions has gained much less attention. This multi-regional CPP problem can be considered as a variant of the (multiple) traveling salesman problem (TSP) enhanced with CPP. Previously, we have studied the case of a single UAV. In this paper, we extend our previous studies to further consider multiple UAVs with energy constraints. To solve this new path planning problem, we develop two approaches: 1) a branch-and-bound (BnB) based approach that can find (near) optimal tours and 2) a genetic algorithm (GA) based approach that can solve large-scale problems efficiently under different objectives. Comprehensive theoretical analyses and computational experiments demonstrate the promising performance of the proposed approaches in terms of optimality and efficiency.
Author	Chen, Jun Xie, Junfei
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SubjectTerms	Approximation algorithms Autonomous aerial vehicles branch and bound Constraints Coverage path planning genetic algorithm Genetic algorithms Military applications Monitoring multiple regions multiple unmanned aerial vehicles Optimization Path planning Robots Task analysis Traveling salesman problem Unmanned aerial vehicles
Title	Multiregional Coverage Path Planning for Multiple Energy Constrained UAVs
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