Journal of System Simulation ›› 2024, Vol. 36 ›› Issue (5): 1199-1210.doi: 10.16182/j.issn1004731x.joss.22-1546

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Deep Learning Based Local Path Planning Method for Moving Robots

Liu Zesen(), Bi Sheng(), Guo Chuanhong, Wang Yankui, Dong Min   

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
  • Received:2022-12-29 Revised:2023-03-06 Online:2024-05-15 Published:2024-05-21
  • Contact: Bi Sheng;


In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the average curvature change rate of path and the distance between robot and obstacle as metrics is also proposed. Experiments show that the proposed approach has good local path generation capability in both simulated and real scenes.

Key words: robot navigation, path planning, real-time obstacle avoidance, deep learning

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