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Multi-Sensor Fusion Based on BPNN in Quadruped Ground Classification
Huang Zhuhui1,2; Wang Wei1
2017
会议名称2017 IEEE International Conference on Mechatronics and Automation
页码1620-1625
会议日期2017
会议地点Takamatsu, Japan
摘要Appropriate perception of different ground substrates plays an essential role in realizing adaptive quadruped locomotion. In this paper, we propose a multi-sensor fusion
method based on Back Propagation Neural Network (BPNN) using in real-time ground substrate classification for adaptive quadruped walking. In order to collect the body gyro information, foot-ground contact force, Direct Current (DC) motor information and joint angle to train the network, we present the enhanced walk strategy with Center of Gravity (COG) adjustment method with 6-axis motion sensor feedback and realize steady walk gait on different ground substrates. Using these method, the quadruped robot Biodog realizes multi-sensor information collection while walking on six different ground substrates. Then we train the BPNN using the collected data after calculation and normalization. In network training, about 99.83% samples have been classified correctly using BPNN. In real-time testing, about 98.33% has been classified successfully using trained BPNN.

关键词Trajectory Planning Quadruped Robot Terrain Classification Bpnn Multi-sensor Fusion
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20932
专题智能感知与计算研究中心
作者单位1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100049, China
推荐引用方式
GB/T 7714
Huang Zhuhui,Wang Wei. Multi-Sensor Fusion Based on BPNN in Quadruped Ground Classification[C],2017:1620-1625.
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