LAUSR

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.