既製センサと視覚姿勢推定によるビンピッキング作業セル：World Robot Summit 2018キッティングタスクのケーススタディ
Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task
最新の視覚センサと姿勢推定アルゴリズム、力覚ベースの把持を組み合わせたビンピッキング作業セルを構築し、WRS 2018のキッティングタスクで競技全チームを上回るスコアを達成した。
著者: Frederik Hagelskjær, Kasper Høj Lorenzen, Dirk Kraft
分類: cs.RO, cs.CV
原文アブストラクト
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.
