Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (4): 361-371.doi: 10.11959/j.issn.2096-6652.202039

• Special Issue: Deep Reinforcement Learning • Previous Articles     Next Articles

Motion planning for hexapod robot using deep reinforcement learning

Huiqiao FU1, Kaiqiang TANG1, Guizhou DENG2, Xinpeng WANG2, Chunlin CHEN1   

  1. 1 School of Management and Engineering, Nanjing University, Nanjing 210046, China
    2 School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
  • Revised:2020-12-04 Online:2020-12-15 Published:2020-12-01
  • Supported by:
    The National Natural Science Foundation of China(71732003);The National Natural Science Foundation of China(62073160);The National Key Research and Development Program of China(2018AAA0101100);The 4th Pre-Research Project of Manned Space(030602)


Hexapod robot have multiple redundant degrees of freedom and are suitable for complex unstructured environments.Discrete environments, as a harsh special case of unstructured environments, require hexapod robots to have more efficient and reliable motion strategies.A plane random plum-blossom pile environment was taken as an example.A random starting point and a target area were set, and the deep reinforcement learning algorithm was applied to plan a motion strategy for a hexapod robot in theplane plum-blossompile environment.To speed up the training process, a deep deterministic policy gradient algorithm with a prioritized experience replay mechanism was used.Finally the policy was verified in a real environment.The results show that the planned motion strategy can make the hexapod robot move efficiently and smoothly from a starting point to a target area in aplane plum-blossom pile environment.This work lays the foundation for the precise motion planning of hexapod robots in the real discrete environment.

Key words: hexapod robot, motion planning, deep reinforcement learning

CLC Number: 

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