Suction Point Selection Algorithm Based on Point Cloud for Plastic Waste Sorting

Abstract

This study proposes a novel decision-making algorithm pertaining to picking locations for a suction grasping robot. The algorithm has been developed for plastic waste sorting facilities. Suction grippers are widely used but it becomes difficult for the grippers to pick up randomly crumpled objects. An optimal grasping point selection based on the quantitative evaluation of grasp quality pertaining to estimated contact zone and adjustable cost function has been proposed to solve the aforementioned problem. The geometrical characteristic of the suction cup is used for the donut-shaped evaluation group. The algorithm processes the input point cloud based on candidate generation, candidate evaluation, and the selection of final picking point for robustness. The algorithm structure and underlying logic is explained in detail. The performance change with respect to the cost function, suction cup type, and estimated contact zone has been experimentally investigated. The algorithm achieves a high success rate in terms of suction grasping, and thus, parallel manipulators can rapidly execute pick and place motion for efficient sorting. This study will help expand the use of robotic arms with suction grippers by enabling a wider span of objects to be processed.