An Improved Acceleration-Level Scheme with Noise Rejection Capability for Repetitive Motion Planning of Redundant Robot Manipulators

Abstract

The research on repetitive motion planning (RMP) of redundant robot manipulators has realized great success. However, noise is generally ignored in the design of RMP schemes at velocity or acceleration level. Noise can lead to significant deviations in key parameters such as position, velocity and acceleration, causing these schemes to be ineffective as they may result in unstable or inaccurate motion. In this paper, with the consideration of additive noise, an improved formulation of the acceleration-level RMP (ALRMP) scheme is proposed and studied for redundant robot manipulators. Specifically, by utilizing the integral of the end-effector tracking error and velocity error, a new acceleration-level equality criterion is established. Based on the equality criterion, the improved ALRMP scheme with noise rejection capability is developed for redundant robot manipulators. Such an improved scheme is then rewritten as a quadratic program and is solved via a neural network. Comparative simulation results on the UR5 robot manipulator in the different cases of noise further validate the effectiveness and superiority of the improved ALRMP scheme over the previous scheme.