Improved Hybrid A* Algorithm Obstacle Avoidance Strategy Based on Reinforcement Learning
Changshou Xu, Zuojun Liu, Chaofang Hu, Xinxin Li · 2023
To address the problem that the traditional obstacle avoidance algorithm does not consider the dynamics constraints and the algorithm planning is not high in real-time, an improved Hybrid A* algorithm based on the reinforcement learning method is proposed. Firstly, the time performance of the Hybrid A* algorithm is improved by remodeling the weight coefficients of the algorithm's heuristic function. Secondly, the algorithm time optimality and performance optimality metric models are established. The corresponding state-action and reward functions are designed incombination with the deep deterministic policy gradient method to train the weight coefficients. Finally, the improved Hybrid A* algorithm is simulated and validated in static and dynamic obstacle environments respectively. The simulation results show effective improvement of the time performance while ensuring optimal path planning results.