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Multi-Sensor Fusion Based on BPNN in Quadruped Ground Classification
Huang Zhuhui1,2; Wang Wei1
Conference Name2017 IEEE International Conference on Mechatronics and Automation
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
Document Type会议论文
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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