Fast and Robust Bin-picking System for Densely Piled Industrial Objects

Abstract

Objects grasping, also known as the bin-picking, is one of the most common tasks faced by industrial robots. While much work has been done in related topics, grasping randomly piled objects still remains a challenge because much of the existing work either lacks in robustness or costs too much resource. In this paper, a fast and robust bin-picking system is developed to grasp densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of applications with noise (DBSCAN) algorithm, improved by combining the region growing algorithm as well as using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and for fine registration, the iterative closest point (ICP) algorithm is used. We propose grasp risk score (GRS) to evaluate each object by the collision probability, the stability of object and the whole pile's stability. Through real test with Anno robot, our method is verified to be advanced in speed and robustness.

Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

“…In bin-picking settings, a surrounding-aware visual perception system in a combination with the correct approach and retreat movements is a remarkably important part of the navigation system. Objects are randomly distributed, and unintended collisions that can occur with the bin, other environmental elements and the robot itself should be strictly avoided [29].…”