Research on UAV Regional Reconnaissance Coverage Path Planning for Mountain Environments

Abstract

To address the problems of poor coverage quality and low reconnaissance efficiency in UAV regional reconnaissance tasks under complex mountain environments, an efficient regional coverage path planning method is proposed. Firstly, a 3D coverage and occlusion model is constructed, and a viewpoint generation method based on the greedy strategy is adopted to screen the optimal viewpoint set. Secondly, the Adaptive Chaotic Dung Beetle Optimization Algorithm (ACDBO) is proposed to solve the optimal order of path points. Through chaotic mapping initialization, nonlinear boundary convergence factor and parameter adaptive adjustment strategy, the global optimization ability and convergence speed of the algorithm are improved, and the optimization objectives comprehensively consider indicators such as path length and deflection angle. Simulation experiments show that the proposed method is superior to traditional methods in terms of the number of viewpoints and path length; compared with the original Dung Beetle Optimization Algorithm and other comparison algorithms, the ACDBO algorithm has better convergence performance and higher planned track quality, and can effectively complete the UAV regional reconnaissance coverage task under mountain environments.