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Multi-Sensor Fusion Based on BPNN in Quadruped Ground Classification
Huang Zhuhui1,2; Wang Wei1
2017
Conference Name2017 IEEE International Conference on Mechatronics and Automation
Pages1620-1625
Conference Date2017
Conference PlaceTakamatsu, Japan
AbstractAppropriate 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.

KeywordTrajectory Planning Quadruped Robot Terrain Classification Bpnn Multi-sensor Fusion
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20932
Collection智能感知与计算研究中心
Affiliation1.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
Recommended Citation
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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