Automatic joint motion planning of 9-DOF robot based on redundancy optimization for wheel hub polishing. (June 2023)

• Record Type: Journal Article
• Authors: Liu, Zhiheng; Li, Ruifeng; Zhao, Lijun; Xia, Yi; Qin, Zhonghao; Zhu, Kui
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 81(2023)
• Publication Date: 2023-06
• Subjects: Robotic polishing system -- Wheel hub -- Offline programming -- Kinematic redundancy -- Joint motion planning; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102500
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: The robotic polishing system with a 6-axis robot and a 3-axis actuator for wheel polishing. We propose an automatic joint motion planning method for a 9-DOF robot to achieve the shortest processing time. A improved DP algorithm is proposed for offline programming of hub polishing. Robot redundancy optimization uses a combined SCA-GBFS algorithm to obtain the optimal joint motion paths. Abstract: Automatic joint motion planning is very important in robotic wheel hub polishing systems. Higher flexibility is achieved based on the joint configuration with multiple solutions, which means that the robot has kinematic redundancy for machining tasks. Redundant joints can be used to optimize the motion of the robot, but less research has been done on multi-dimensional redundant optimization. In this paper, a 6-axis robot with a 3-axis actuator is designed for wheel hub polishing. We propose an automatic joint motion planning method for a nine-axis industrial robot to achieve the shortest processing time. Firstly, offline programming is designed to generate paths for the complex surface of the hub. In order to reduce the machining path points on the surface of the hub, a improved Douglas-Peucker (DP) algorithm is proposed, which can take into account the change of the path point posture. Secondly, the Greedy Best First Search (GBFS) and Sine cosine algorithm (SCA) are combined to find the optimal joint motion efficiently. Moreover, we use nested SCA for comparison to test whether the combined algorithm can avoid local optima. Finally, the performance and computational efficiency of the method are validated in both simulation and real environments based on the hub surface.