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Structural parameter identification for 6 DOF industrial robots

机译:6自由度工业机器人的结构参数辨识

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To decrease the movement uncertainty of industrial robots, a parameter identification method based on the Denavit-Hartenberg (DH) model is presented in this paper, where the redundant parameters are particularly addressed in the identifier procedure. In order to be consistent with the kinematic model used in robot controllers, we use DH method to establish the kinematic model instead of the modified DH (MDH) method that was used in most identification schemes. The kinematic model of a 6 degree of freedom (DOF) industrial robot is first developed, which is linearized to obtain the parameter identification coefficient matrix. Further analysis shows that this matrix is not with full rank, which means some parameters in this matrix are linearly dependant. This fact makes the direct identification of unknown parameters in this matrix unfeasible. To solve this problem, singular value decomposition (SVD) is used to determine the redundant parameters, which are then removed from the matrix. Then, an alternative identification algorithm with a modified least-square scheme is suggested to estimate the structural parameters of the robot. For this purpose, an identification calculation scheme is designed to minimize the residual movement uncertainties. Experimental studies based on a 6 DOF industrial robot show that the proposed identification method, which detects and removes the redundant parameters, can greatly reduce the residual movement uncertainties and calculation costs. Thus, this newly proposed method can improve the movement accuracy of the industrial robot significantly.
机译:为了减少工业机器人的运动不确定性,本文提出了一种基于Denavit-Hartenberg(DH)模型的参数辨识方法,其中冗余参数在识别过程中得到了特别的解决。为了与机器人控制器中使用的运动学模型保持一致,我们使用DH方法来建立运动学模型,而不是大多数识别方案中使用的改进的DH(MDH)方法。首先开发了6自由度(DOF)工业机器人的运动学模型,将其线性化以获得参数识别系数矩阵。进一步的分析表明,该矩阵不是满秩的,这意味着该矩阵中的某些参数是线性相关的。这一事实使得在该矩阵中直接识别未知参数变得不可行。为了解决此问题,使用奇异值分解(SVD)确定冗余参数,然后将其从矩阵中删除。然后,提出了一种改进的最小二乘方案的替代识别算法来估计机器人的结构参数。为此,设计了一种识别计算方案,以最大程度地减少残余运动的不确定性。基于六自由度工业机器人的实验研究表明,所提出的识别方法可以检测并去除冗余参数,可以大大减少残余运动的不确定性和计算成本。因此,该新提出的方法可以显着提高工业机器人的运动精度。

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