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

TL;DR

This paper presents a fast, robust bin-picking system for densely piled objects using improved clustering, PCA, ICP, and a novel grasp risk score, verified through real robot tests.

Contribution

It introduces an adaptive bin-picking system combining enhanced clustering, registration, and a new grasp risk score for improved speed and robustness.

Findings

System achieves faster grasping in dense piles.

Demonstrates high robustness in real-world tests.

Outperforms existing methods in speed and stability.

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 lack robustness or costs too much resource. In this paper, we develop a fast and robust bin-picking system for grasping densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of application with noise (DBSCAN) algorithm, which is improved by combining the region growing algorithm and using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and iterative closest point (ICP) for fine registration. We propose a grasp risk score (GRS) to evaluate each object by the collision probability, the stability of the object, and the whole pile's stability. Through real tests with the Anno robot, our method is verified to be advanced in speed and robustness.