Dynamic Filtered Path Tracking Control for a 3RRR Robot Using Optimal Recursive Path Planning and Vision-Based Pose Estimation

This study proposes a dynamic filtered path tracking control schema for a 3RRR planar parallel robot to reduce the positioning errors caused by various sources such as backlash in the mechanical system, system nonlinearities, uncertainties, and unknown disturbances. The optimal recursive algorithm is implemented absolutely in path planning based on the optimization problem. Compared to other methods, this algorithm not only helps the robot avoid singularities but also ensures the shortest movement distance of links thanks to the optimization in path planning. The vision system using the 3D Intel RealSense camera D435i is proposed to provide the visual measurement as feedback for the pose estimation of the 3RRR planar parallel robot. The unscented Kalman filter is designed to reduce the errors of the pose estimation due to the camera's intrinsic parameters, the vibration of the mechanical system, and unknown noises. The establishment of important equations and parameters of the unscented Kalman filter is detailed in this study. The simulation, experiment, and comparison are conducted to verify the effectiveness and feasibility of the proposed approaches.