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Motion Control of Hyper Redundant Robots with Learning Control Scheme Based on Linear Combination of Error History

基于误差历史线性组合的学习控制方案的超冗余机器人运动控制

基本信息

批准号：
18560243
负责人：
IWATSUKI Nobuyuki
金额：
$ 2.45万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

项目摘要

Aiming to establish effective motion control scheme for hyper redundant robot by learning control based on linear combination of error history, new methods to set suitable subtasks and to obtain suitable initial configuration of the robots, the optimum motion control based on dexterity of the robots, and flexibility control of the robots with elastic elements are discussed. The obtained results are summarized as follows :(1)For hyper redundant planar serial manipulators, new methods to set subtask so as to pass obstacles away or assist main task were proposed and formulated.(2) A new 'Backward learning scheme', in which a converged configuration obtained with forward learning was set as initial configuration and the learning processes were repeated, was proposed so as to obtain suitable initial configuration.(3)Quantitative indices to evaluate assistability for main task and movability of links were derived from column vector of Jacobian matrices of joints. By setting the indices as objective functions, the joint inputs were optimized with gradient projection method and learning control.(4) new flexibility control method to specify both of output displacement and stiffness distribution of a closed-loop redundant manipulator with elastic passive joints was formulated and was experimentally examined. A prototype can then manipulate soft/hard objects while controlling output flexibility.It was thus revealed that the learning control based on linear combination of error history could realize motion and output force control while utilizing redundancy.

针对基于误差历史线性组合的学习控制方法建立有效的超冗余度机器人运动控制方案，讨论了设置合适子任务和获得合适初始构型的新方法、基于机器人灵巧性的最优运动控制方法以及具有弹性元件的机器人柔性控制方法。结果表明：(1)针对超冗余度平面串联机械臂，提出并制定了设置子任务以避开障碍物或辅助主任务的新方法。(2)提出了一种新的“后向学习方案”，将前向学习得到的收敛构型作为初始构型，并重复学习过程，以获得合适的初始构型。(3)根据关节雅可比矩阵的列向量，导出了评价关节主任务辅助性和可动性的定量指标。以指标为目标函数，采用梯度投影法和学习控制对联合输入进行优化。(4)提出了一种新的柔性控制方法，以确定具有弹性被动关节的闭环冗余度机械臂的输出位移和刚度分布，并进行了实验验证。然后，原型可以在控制输出灵活性的同时操纵软/硬对象。结果表明，基于误差历史线性组合的学习控制可以在利用冗余的同时实现运动和输出力的控制。