Abstract
Due to factors such as nonlinearity, parameter and disturbance uncertainty, actuator failure and other factors in the robot control system, it is difficult to ensure high-precision trajectory tracking of the system based on the traditional system model-based algorithm. In this paper, a method of robot vision localization based on iterative Kalman particle filter is proposed, which realizes the global positioning of the robot and carries out experimental verification. The results show that the positioning accuracy and real-time performance of the robot mobile navigation can meet the requirements. The task of performing industrial robots is repetitive, and iterative learning control technology is introduced to adapt to the repeated dynamic characteristics of industrial robots when they repeatedly perform work tasks. An adaptive iterative learning interactive multi-mode trajectory tracking controller is proposed. According to the error and error derivative of the trajectory tracking, the learning amount is obtained, which is used to correct the output of the PD controller, thereby improving the trajectory tracking accuracy. The feasibility and effectiveness of the proposed algorithm are verified by simulation results.
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