基于强化学习的改进三维A*算法在线航迹规划

In order to address the problem of high requirements for real-time performance and optimality of real-time path planning, a three-dimensional A∗ algorithm is improved based on the reinforcement learning method. Firstly, the shrinkage factor is introduced to ameliorate the heuristic information weighting method of the improved cost function, so as to improve the time performance. Secondly, a measurement model is established to measure the real-time performance and optimality of the algorithm. Combined with the deterministic policy gradient method, the action-state and reward functions are designed to optimize the shrinkage factor. Finally, the improved three-dimensional A∗ algorithm is simulated in multiple scenarios, and the simulation results show that the improved algorithm can ensure the optimality of the track results and effectively improve the time performance of the algorithm.