Hybrid path planning for UAV using rapidly-expanding random trees and proximal policy optimization in complex environments

This paper proposes a hybrid Unmanned Aerial Vehicle (UAV) path planning method that combines the Rapidly-exploring Random Tree (RRT) algorithm with Proximal Policy Optimization (PPO). The proposed method aims to enhance the efficiency and adaptability of UAV path planning in complex and dynamic environments. The RRT algorithm excels at quickly generating a feasible path from a start point to a goal point. However, the quality of its paths is often suboptimal, and it lacks adaptability in dynamic settings. In contrast, PPO, a deep reinforcement learning algorithm, optimizes paths through iterative policy updates, enabling the UAV to adapt to environmental changes. Our approach first employs RRT to generate an initial path, which is subsequently refined by PPO to improve smoothness and adaptability. Experimental results demonstrate that the RRT-PPO hybrid method performs favorably in terms of path length, computational time, and obstacle avoidance capability, effectively improving the task completion efficiency of UAVs in complex environments.