Adaptive Interfered Fluid Dynamic System Algorithm Based on Deep Reinforcement Learning Framework

In this paper, adaptive interfered fluid dynamic system algorithm (AIFDS) is proposed for unmanned aerial vehicle (UAV) path planning in dynamic obstacle environment, inspired by the natural flow to avoid rocks. In AIFDS, UAV is regarded as an agent. Through the interaction with the environment, it gradually learns how to adjust the flow field, so as to plan a path with high safety, short distance and short execution time in advance. AIFDS can be combined with almost all reinforcement learning algorithms with continuous action space. This paper studies the combination of AIFDS with SAC, DDPG, PPO, TD3 algorithm. Experiments are carried out in the environment with multiple dynamic obstacles, and the results show that AIFDS has a bright performance in the aspect of path safety.