【摘要】 先进的机器人由计算机执行程序来完成各种作业，靠计算关节变量的函数得到手爪的位姿，这些函数一般不准确，使计算值与实际值有较大误差；重复精度０．１ｍｍ的机器人该误差可能达到１０ｍｍ。已有的机器人运动学误差补偿方法需要分析误差来源，使其参数化，并辨识这些参数，六自由度机器人的这种参数已多达７２个之多。本文提出一种机器人运动学误差补偿的神经网络模型，利用改进的误差反传（ＢＰ）学习算法，在ＲＭ－５０１机器人上进行实验，实验结果表明，所提出的方法是有效和可行的。更多还原

【Abstract】 Advanced industrial robots are commanded to accomplish different tasks with program that is ex-ecuted in computer. The operating software provides users with information on position and orientation of theend effectors by computing them as functions of the joint variables. These functions are generally not exact enough such that differences between the computed and the actual position can be significant. The differenceas high as 10mm may be possible for the robot with roughly 0. 1mm repeatability. Some other methods forcalibrating and compensating robot kinematic error need to analysis error sources,to parametrize the effectsof these error and to estimate these parameters,usually there can be up to 72 error parameters for a six-jointrobot.In this paper,a neural network model for compensating robot kinematic error is proposed.An im-proved BP learning algorithm is also presented.Experimental results in RM- 501,a five-degree-of freedom ar-ticulated robot,show that the proposed method is efficient and feasible.更多还原