BP networks based trajectory planning and inverse kinematics of a reconfigurable Mars rover
Zhang LP(张力平); Ma SG(马书根); Li B(李斌); Zhang Z(张政); Zhang GW(张国伟); Cao BG(曹秉刚)
2005
会议名称2nd International Symposium on Neural Networks
会议日期May 30-June 1, 2005
会议地点Chongqing, China
页码247-252
中文摘要The inverse kinematics of series manipulators presents an inherent complexity due to their various structures and kinematics constraints. To a novel reconfigurable Mars rover's arm, the inverse kinematics had been solved by numerical method combined with space geometry relations, but the complex calculating process can not be utilized in real-time control. In this paper, some actions in common use are programmed in the child rover, and the BP neural network was used to solve the inverse kinematics and trajectory planning. To a desired trajectory, some solutions by direct kinematics algorithms and geometry relations corresponding to the trajectory were used as the BP network's training data set, and the non-linear mapping from joint-variable space to operation-variable space was obtained with iterative training and learning method. The results show the output of training is consistent with the desired path, and the obtained reference trajectory is smooth.
收录类别SCI ; EI ; CPCI(ISTP)
产权排序2
会议主办者Chongqing Univ, SW Normal Univ, Chongqing Univ, Posts& Telecommun, SW Agr Univ, Chongqing Educ Coll, Chinese Univ Hong Kong, Asia Pacific Neural Network Assembly, European Neural Network Soc, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, Natl Nat Sci Fdn China, K C Wong Educ Fdn Hong Kong
会议录ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS
会议录出版者SPRINGER-VERLAG
会议录出版地BERLIN
语种英语
ISSN号0302-9743
ISBN号3-540-25914-7
WOS记录号WOS:000230167700039
研究领域[WOS]Computer Science
WOS标题词Science & Technology ; Technology
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/8599]  
专题沈阳自动化研究所_机器人学研究室
推荐引用方式
GB/T 7714
Zhang LP,Ma SG,Li B,et al. BP networks based trajectory planning and inverse kinematics of a reconfigurable Mars rover[C]. 见:2nd International Symposium on Neural Networks. Chongqing, China. May 30-June 1, 2005.
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