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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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 10; pp. 17366 - 17381
Main Authors: Xie, Junfei, Chen, Jun
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3160402