An adaptive heuristic algorithm with a collaborative search framework for multi-UAV inspection planning

Abstract

Multi-UAV inspection path planning has become an important research topic for completing inspection tasks before the data acquisition deadline. In this study, we propose an adaptive heuristic algorithm with a collaborative search framework named Sa-VCO to solve the multi-UAV inspection path planning problem. Our study includes three main novelties. First, we design a region-gridding disperse approach that transforms the primitive target regions into a set of standard target subregions, allowing the target regions with greater costs to be inspected by multiple UAVs. Second, we propose an adaptive initial solution generation strategy using the information of graph structure constructed by all targets to reduce redundant computing. Third, we established a collaborative search framework to enhance search efficiency and increase population diversity. A large number of multiple-perspective comparative experiments are provided to test Sa-VCO's performance, and the comparison results demonstrate that Sa-VCO achieves better results than other advanced algorithms, especially on large-scale data sets.