- 综合性科技类中文核心期刊
- 中国科技论文统计源期刊
- 中国科学引文数据库来源期刊
- 中国学术期刊文摘数据库（核心版）来源期刊
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| Citation: |
ZHU Xiaoqing, CHEN Lu, RAN Dengyu, QIAN Yizhao, WANG Mingchao, BIE Tong. Autonomous Gait Learning of Hexapod Robot Based on Reward Shaping[J]. Journal of Beijing University of Technology, 2021, 47(2): 120-126. DOI: 10.11936/bjutxb2019110007
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To improve the autonomous learning ability of robots, a novel autonomous gait learning algorithm based on reward shaping was proposed. First, the mechanical structure of gait synchronous hexapod robot was introduced, with unique multi-layer steel leg design and special mechanical synchronous transmission scheme, which laid a good foundation for robots' gait learning research. Second, a robot gait learning algorithm based on reward shaping was presented, allowing the robot to learn skills in an unknown environment. A concise reward and punishment function, which was based on the principle of simplicity, was designed. Finally, experiments performed on MATLAB and ADAMS joint simulation platform verified the proposed algorithm. Two reasonable convergent results were presented when speed and height, respectively, were the main evaluating indicators. Moreover, the hexapod robot independently learned the difficult skills to move over the blocks with the proposed algorithm.
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