Tool Center Point Calibration via Posture-Sequence Particle Swarm Optimization

Abstract

Robots’ manipulation accuracy is heavily determined by the calibration accuracy of the tool center point (TCP). This article proposes posture-sequence particle swarm optimization (PS2O) for TCP calibration. First, the mechanism of the condition number and the minimum eigenvalue of the regression matrix on the calibration accuracy are analyzed. Second, a multiobjective optimization problem is constructed based on the condition number, the minimum eigenvalue, and the sum of adjacent posture distances. Finally, an optimized posture sequence is obtained by applying the particle swarm optimization (PSO) algorithm to the constructed multiobjective optimization problem. Simulations and experiments demonstrate that the error of TCP calibration can be characterized by the condition number and the minimum eigenvalue of the regression matrix. Compared with random posture sampling, the optimized posture sequence for sampling reduces the norm of the calibration error by 38.66%. Thus, PS2O has great potential to improve robots’ manipulation accuracy.