Abstract
AbstractTo tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit‐Hartenberg and the small displacement screw methods, the kinematic model of a six‐degree‐of‐freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub‐population cooperation under the multi‐attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
