Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task

TL;DR

This paper demonstrates that combining advanced vision sensors and pose estimation algorithms enables effective off-the-shelf bin picking, achieving superior performance in a competitive robotics challenge.

Contribution

The study presents a complete bin-picking workcell utilizing new vision sensors and algorithms, showing improved success in a major robotics competition.

Findings

Successfully performed bin picking in the WRS 2018 challenge

Achieved higher scores than all competing teams

Validated the effectiveness of current vision and grasping technologies

Abstract

The World Robot Summit 2018 Assembly Challenge included four different tasks. The kitting task, which required bin-picking, was the task in which the fewest points were obtained. However, bin-picking is a vital skill that can significantly increase the flexibility of robotic set-ups, and is, therefore, an important research field. In recent years advancements have been made in sensor technology and pose estimation algorithms. These advancements allow for better performance when performing visual pose estimation. This paper shows that by utilizing new vision sensors and pose estimation algorithms pose estimation in bins can be performed successfully. We also implement a workcell for bin picking along with a force based grasping approach to perform the complete bin picking. Our set-up is tested on the World Robot Summit 2018 Assembly Challenge and successfully obtains a higher score compared with all teams at the competition. This demonstrate that current technology can perform bin-picking at a much higher level compared with previous results.