A Low-Cost, High-Speed, and Robust Bin Picking System for Factory Automation Enabled by a Non-Stop, Multi-View, and Active Vision Scheme

TL;DR

This paper presents a low-cost, high-speed bin picking system that integrates multi-view active vision with robot motion, significantly improving robustness and efficiency in factory automation for metallic objects.

Contribution

It introduces a novel

Findings

Sensing scheme completes within 1.682 seconds on CPU.

Average picking success rate exceeds 97.75%.

Sensing accounts for only 0.635 seconds in takt time.

Abstract

Bin picking systems in factory automation usually face robustness issues caused by sparse and noisy 3D data of metallic objects. Utilizing multiple views, especially with a one-shot 3D sensor and "sensor on hand" configuration is getting more popularity due to its effectiveness, flexibility, and low cost. While moving the 3D sensor to acquire multiple views for 3D fusion, joint optimization, or active vision suffers from low-speed issues. That is because sensing is taken as a decoupled module from motion tasks and is not intentionally designed for a bin picking system. To address the problems, we designed a bin picking system, which tightly couples a multi-view, active vision scheme with motion tasks in a "sensor on hand" configuration. It not only speeds up the system by parallelizing the high-speed sensing scheme to the robot place action but also decides the next sensing path to maintain the continuity of the whole picking process. Unlike others focusing only on sensing evaluation, we also evaluated our design by picking experiments on 5 different types of objects without human intervention. Our experiments show the whole sensing scheme can be finished within 1.682 seconds (maximum) on CPU and the average picking complete rate is over 97.75%. Due to the parallelization with robot motion, the sensing scheme accounts for only 0.635 seconds in takt time on average.