Reinforcement learning-integrated evolutionary algorithm for enhanced unmanned aerial vehicle coverage path planning

The rapid development of unmanned aerial vehicle (UAV) technologies has led to their increased utilization across various industries. In search and rescue (SAR) missions, UAVs play a critical role in overcoming mobility constraints in search environments, particularly in time-sensitive situations such as maritime operations. To enhance the efficiency of search missions, this study addresses the Coverage Path Planning (CPP) problem for multiple UAVs in irregularly shaped search areas. We propose a novel CPP framework consisting of two main phases. In Phase 1, a reinforcement learning-integrated evolutionary algorithm is introduced for search area decomposition, aiming to minimize the area of the grid map exceeding the search area. Specifically, proximal policy optimization-based particle swarm optimization (PPO–PSO) is employed to effectively adapt to complex and irregular shapes. In Phase 2, a Mixed Integer Linear Programming (MILP) model is formulated to minimize mission completion time while ensuring collision avoidance and efficient task allocation for multiple UAVs. The proposed methodology was validated through 15 experimental scenarios, including real-world maritime environments, and demonstrated superior performance compared to existing methods in managing irregularly shaped search areas.