Abstract
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.
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Acknowledgement
This research has been funded in part by the National Natural Science Foundations of China under Grant 61673042 and 61175084, and the Aeronautical Science Foundation of China under Grant 2018ZC51031.
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Zhang, Y., Wang, H. (2022). Adaptive Interfered Fluid Dynamic System Algorithm Based on Deep Reinforcement Learning Framework. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_139
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DOI: https://doi.org/10.1007/978-981-16-9492-9_139
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