Intelligent selection and optimization of measurement poses for a comprehensive error model identification of 6-DOF serial robot

摘要

Non-geometric errors mainly caused by the joint compliance should be identified and compensated as well as geometric errors to improve the accuracy. This paper presents a new comprehensive error model consisting of both geometric and compliance parameters. A new approach is proposed for intelligent selection and optimization of measurement poses based on interference detection method and linearly decreasing weight particle swarm optimization (LinWPSO) algorithm. Simulation results on a 6-DOF serial industrial robot demonstrate that using the optimal measurement poses can significantly improve the calibration accuracy and measurement efficiency.