基于深度强化学习的三维路径规划算法

合理的路线选择是智能体三维路径规划研究领域的难点。现有路径规划方法存在不能很好地适应未知地形，避障形式单一等问题。针对这些问题，提出了一种基于LSTM-PPO的智能体三维路径规划算法。利用虚拟射线探测仿真环境，并将收集到的状态空间和动作状态引入长短时记忆网络。通过额外的奖惩函数和好奇心驱动让智能体学会跳跃通过低矮障碍物，避开大型障碍物。利用PPO算法的截断项机制使得规划策略更新的幅度更加优化。实验结果表明，该算法是可行的，能够更加智能合理地选择路线，很好地适应存在多样障碍物的未知环境。

3D Path Planning Algorithm Based on Deep Reinforcement Learning

Reasonable path selection is a difficulty in the field of 3D path planning. The existing 3D path planning methods can not adapt to the unknown terrain, and the obstacle avoidance form is single. In order to solve these problems, a 3D path planning algorithm for agents based on LSTM-PPO is proposed. Virtual ray is designed to detect simulation environment, and the collected state space and action states are introduced into Long Short-Term Memory Networks（LSTM）. Through the extra reward function and intrinsic curiosity module, the agent can learn to jump through low obstacles and avoid large obstacles. Using the PPO’s clipped surrogate objective to optimize the update range of planning strategy. The results show that the algorithm is feasible, more intelligent and more reasonable for path planning, and can adapt well to the unknown environment with many obstacles.