Robot manipulators enable large-scale factory automation of simple and repeated\ntasks. Each manipulation is the result of the robot design and the command inputs provided\nby the operator. In this study, we focus on the accuracy improvement of practical\nrobot manipulation under uncertainty, resulting in path-specific error values. Existing\ntechniques for reducing the errors use high-precision sensors and measurements to\nobtain the values of a manipulator to provide feedback control. Instead of compensating\nerrors in operation, this study designs a calibration table to obtain the error value\nfor a designated path. This error is then used to adjust important parameters in the kinematic\nclosed chain models of a manipulators via optimization. The proposed method\nreduces the cost and the dependence on the calibration process. Experimental results\nshow that the overall accuracy of the manipulator is improved. The proposed method\ncan also be extended to develop the optimal robotic manipulation planning and reliability\nassessment in the future.
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