Deep reinforcement learning-based reactive trajectory planning method for UAVs

In order to improve the ability of avoiding dynamic threats during the flight of unmanned aerial vehicles (UAVs), a deep reinforcement learning-based reactive trajectory planning method is proposed in this paper. Firstly, a constrained Rapidly-exploring Random Tree-Connect algorithm (C-RRT-Connect) is proposed as the basic algorithm of reactive trajectory planning to globally plan for avoiding static obstacles in the environment. The C-RRT-Connect algorithm introduces the idea of target attraction to constrain the optimal growth point in the RRT-Connect algorithm. Then, based on the global trajectory, the local optimization is carried out according to the dynamic threats detected by the UAV during the flight. According to the real-time relative state between the UAV and the detected dynamic threat, the reaction sampling points and directional coefficients for avoiding the corresponding dynamic threat are generated online via the action network trained with the depth deterministic policy gradient algorithm (DDPG). And then the local trajectory is adjusted to modify the flight trajectory of the UAV to achieve reactive obstacle avoidance. The simulation experiment firstly compares the global trajectory planning performance of C-RRT-Connect and RRT-Connect in static environment, and secondly compares the local trajectory planning performance of DDPG algorithm and the artificial potential field method in dynamic environment. The experimental results show that in static environment, C-RRT-Connect algorithm has faster searching speed, less invalid samples and higher searching trajectory quality than RRT-Connect algorithm; In a dynamic environment, DDPG algorithm reduces the average running time by about 26% compared with the artificial potential field method, and has a stronger ability to evade dynamic threats in real time.