Disaster Relief Coverage Path Planning for Fixed-Wing UAV Based on Multi-Selector Genetic Algorithm and Reinforcement Learning

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively.