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Article type: Research Article
Authors: Xue, Junxiaoa | Kong, Xiangyanb; * | Wang, Gangb | Dong, Boweic | Guan, Haiyangd | Shi, Leie
Affiliations: [a] Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China | [b] School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China | [c] School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China | [d] Academy for Engineering, FuDan University, Shanghai, China | [e] School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
Correspondence: [*] Corresponding author. Xiangyan Kong, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China. E-mail: 22b905071@stu.hit.edu.cn.
Abstract: The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environments. However, the paths formed by grid edges can be longer than the actual shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. In this paper, Matplotlib is used for simulation experiments. The results show that our method has improved considerably in accuracy by 8.11% -43.20% compared with theother methods, and on the length of the path and turning angle by reducing about 50 units and 340 degrees compared with DeepQ Network (DQN), respectively. We also employ Unity 3D to perform simulation experiments in highly uncertain environments, such as squares.
Keywords: path planning, obstacle avoidance, MPC, DDPG, artificial potential field
DOI: 10.3233/JIFS-211999
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1817-1831, 2023
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