旅行商问题
运动规划
计算机科学
路径(计算)
钥匙(锁)
多样性(控制论)
搜救
遗传算法
数学优化
能量(信号处理)
运筹学
工程类
人工智能
算法
机器学习
机器人
数学
计算机网络
统计
计算机安全
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:23 (10): 17366-17381
被引量:14
标识
DOI:10.1109/tits.2022.3160402
摘要
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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