Deep Reinforcement Learning-based Collaborative Multi-UAV Coverage Path Planning

Abstract

Abstract The coverage path planning problem has gained significant attention in research due to its wide applicability and practical value in various fields such as logistics and distribution, smart homes, and unmanned vehicles. This paper focuses on studying the coverage path planning problem under multi-UAV collaboration to maximize the coverage of the mission area within a given time. To address this problem, we propose a multi-objective optimization model and reformulate it with the framework of Decentralized Partially Observable Markov Decision Process (Dec-POMDP). We then employ a multi-agent deep reinforcement learning (MADRL) method to solve the problem. Specifically, we introduce the ε—Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (ε—MADT3), which incorporates an exploration coefficient based on MATD3. This coefficient gradually decays with the number of iterations, allowing for a balance between exploration and exploitation. Numerous simulation results demonstrate that ε—MADT3 outperforms the baseline algorithm in terms of coverage rate and number of collisions.