基于距离误差的机器人参数辨识模型与冗余性分析
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国家自然科学基金项目(51505235)和江苏省自然科学基金青年项目(BK20150844)


Parameter Calibration Model and Redundancy Analysis of Robot Based on Distance Error
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    摘要:

    为避免机器人运动学参数辨识过程中,测量坐标系与机器人基坐标系之间繁琐的坐标变换,首先利用关节旋量的空间几何特性,提出了基于伴随变换的距离误差模型。其次,针对距离误差模型中可辨识参数的冗余性,通过辨识雅可比矩阵的零空间分析,确定了可辨识参数的数目与误差测量方式之间的关系。确定了绕对应关节旋转的测量方式和相对初始位形的测量方式下可辨识参数的数目。最后,对KUKA youBot机器人的运动学参数辨识进行了实验研究,实验结果验证了距离误差模型的有效性和参数冗余性分析的正确性。

    Abstract:

    In kinematic calibration of a serial manipulator, to avoid the complex process and determine the relation between measurement coordinates and robot base coordinates when using pose error, a distanceerror model was proposed, which placed its base on the geometric property of joint screw and error compensation scheme of adjoint transform. Comparing with other distanceerror models such as DH model and MDH model, the proposed model can guarantee the geometric constraints on the joint screw to be naturally satisfied. Furthermore, the physical meanings of the kinematic parameters involved in this model were explicit. As a result, it was relatively easy to evaluate the influence of each kinematic parameter on the distance errors. To enhance the robustness of the model, the kinematic parameters redundancy was studied by investigating the null space of the Jacobian matrix. It was found that the number of independent parameters was determined by the measurement method used to measure the distance errors. Specifically, let r be the number of revolute joints, then, the maximum number of the calibrated parameters was 4r-2, and the number became 2r and 3r-1 when measuring the errors by rotating the corresponding joint and taking the initial configuration as the reference, respectively. In order to verify the effectiveness of the proposed model and the correctness of the redundancy analysis, calibration experiments were performed on KUKA youBot with five degrees of freedom. It was found that the result about calibrated parameters obtained from the theoretic analysis was the same as that of experiment. Meanwhile, the mean distance error was decreased by 116 times after calibration than before calibration. Therefore, the kinematic accuracy of the robot can be greatly improved by the proposed distanceerror model.

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申景金,郭家桢,MASOUD Kalantari.基于距离误差的机器人参数辨识模型与冗余性分析[J].农业机械学报,2018,49(11):372-378. SHEN Jingjin, GUO Jiazhen, MASOUD Kalantari. Parameter Calibration Model and Redundancy Analysis of Robot Based on Distance Error[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(11):372-378.

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  • 收稿日期:2018-05-10
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  • 在线发布日期: 2018-11-10
  • 出版日期: 2018-11-10