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| A Grasping Poses Detection Algorithm for Industrial WorkpiecesBased on Grasping Cluster and Collision Voxels |
| XU Jin1,2, LIU Ning2,3, LI Deping2,3, LIN Longxin1, WANG Gao1,2 |
1. College of Information Science and Technology, Jinan University, Guangzhou 510632, China;
2. Robotics Research Institute, Jinan University, Guangzhou 510632, China;
3. College of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China |
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Abstract Aiming at the grasping problem of scattered and stacked industrial workpieces,a grasping poses detection algorithm based on grasping cluster and collision voxels is proposed.The proposed grasping cluster is a set of continuous grasping poses on the workpiece,and solves the problems of grasping points losing and screening efficiency decreasing caused by using discrete and fixed grasping points in traditional methods.Firstly,the point cloud of scene and the bin are voxelized.Then,the voxels containing bin or point cloud are marked as collision voxels,and the voxels adjacent to the collision voxels are marked as risk voxels,so as to establish a voxelized collision model.After that,according to the geometric properties of the grasping cluster,the candidate grasping poses and corresponding grasping paths are computed.Finally,the collision detection can be quickly completed by detecting the types of voxels that the grasping paths pass through,so as to select the best grasping pose.Based on the proposed algorithm,an integral Bin-Picking system is built,and simulation experiment and actual grasping experiment are performed on various common workpieces from actual industrial situations.The results show that the algorithm can detect safe grasping poses quickly and accurately.The average success rate of grasping reaches 92.2% and the average emptying rate of bins reaches 87.2%,which obtains a significant improvement compared with traditional methods,and there is no collision during the process of grasping,which can meet the requirements of practical industrial applications.
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Received: 25 January 2021
Published: 22 March 2022
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