Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task
This addresses the problem of robotic flexibility in industrial tasks like kitting, though it is incremental as it builds on existing sensor and algorithm advancements.
The paper tackled bin-picking in robotics by using new vision sensors and pose estimation algorithms, resulting in a workcell that achieved a higher score than all teams at the World Robot Summit 2018 competition.
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.