Multi-Sensor Fusion Based on BPNN in Quadruped Ground Classification

CASIA OpenIR > 模式识别实验室

	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	模式识别实验室
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
第一作者单位	中国科学院自动化研究所
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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
PID4804493.pdf（1432KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载