基于深度学习的机器人局部路径规划方法

作者：Liu Zesen; Bi Sheng^*; Guo Chuanhong; Wang Yankui; Dong Min

来源：Xitong Fangzhen Xuebao / Journal of System Simulation, 2024, 36(5): 1199-1210.

DOI：10.16182/j.issn1004731x.joss.22-1546

• deep learning
• path planning
• real-time obstacle avoidance
• robot navigation

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. ? 2024 Acta Simulata Systematica Sinica.

• 单位
  华南理工大学