Robot manipulators enable large-scale factory automation of simple and repeated tasks. Each manipulation is the result of the robot design and the command inputs provided by the operator. In this study, we focus on the accuracy improvement of practical robot manipulation under uncertainty, resulting in path-specific error values. Existing techniques for reducing the errors use high-precision sensors and measurements to obtain the values of a manipulator to provide feedback control. Instead of compensating errors in operation, this study designs a calibration table to obtain the error value for a designated path. This error is then used to adjust important parameters in the kinematic closed chain models of a manipulators via optimization. The proposed method reduces the cost and the dependence on the calibration process. Experimental results show that the overall accuracy of the manipulator is improved. The proposed method can also be extended to develop the optimal robotic manipulation planning and reliability assessment in the future.
Inaccuracy in robot manipulation is a result of various uncertainties. Most methods reduce operation errors by calibrating robot parameters, with little attention on understanding the uncertainty sources in the process. This paper investigates how operation accuracy of robot manipulators can be improved by identifying one of the major uncertainty–joint clearance. We first develop the dynamic model of a Delta robot with joint clearance to obtain the operation error of a given trajectory. Errors with different operating procedures can, therefore, be calculated. We then use a Kriging-based model to relate manipulator performances with joint clearance values. Real-time calibration can then be performed by identifying joint clearance via experiments. Errors can also be reduced using optimal path planning with the calibrated joint clearance. Results show that this method reduces the average error at target points from 0.637 to 0.031 mm for robot manipulators with joint clearances of 0.328, 0.171, and 0.483 mm. This is a 95.1% improvement in accuracy over that for the manipulator before optimization. The proposed method can help manufacturers determine robot quality, and achieve optimal operation in a workspace with improved accuracy.
The positioning accuracy of the empirical robot manipulators is determined by various factors, such as kinematic accuracy, structure rigidity, and controller performance. Here, we report on the development of a new and straightforward technique to calibrate the kinematic parameters of a dual-arm robot under uncertainty. In comparison with other techniques, which generally rely on using other instruments to calibrate the manipulators, the proposed method utilizes the intrinsic characteristics of the dual-arm robot for calibration. In particular, when the two arms grasp each other, a formed closed chain can be operated as the constraint equation for the kinematic parameter optimization of the two arms. In the optimization process, the dual-arm robot has to pose in various configurations to yield better performance, and thus a motion generation strategy of the dual-arm robot is proposed, where one arm serves as the master to track the designated trajectory and the other arm serves as the slave to track the motion of the master arm by using a compliance control strategy. The proposed calibration method was experimentally validated, and the results confirm that the positioning accuracy of both arms can be improved.
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