Robust bin-picking system using tactile sensor
Sho Tajima, S. Wakamatsu, Taiki Abe, Masanari Tennomi, Koki Morita, Hirotoshi Ubata, Atsushi Okamura, Yuji Hirai, Kota Morino, Yosuke Suzuki, Tokuo Tsuji, Kimitoshi Yamazaki, Tetsuyou Watanabe
- Year
- 2019
- Citations
- 11
Abstract
This paper presents a robust bin-picking system utilizing tactile sensors and a vision sensor. The object position and orientation are estimated using a fast template-matching method through the vision sensor. When a robot picks up an object, the tactile sensors detect the success or failure of the grasping, and a force sensor detects the contact with the environment. A weight sensor is also used to judge whether the lifting of the object has been successful. The robust and efficient bin-picking system presented herein is implemented through the integration of different sensors. In particular, the tactile sensors realize rope-shaped object picking that has yet to be made possible with conventional picking systems. The effectiveness of the proposed method was confirmed through grasping experiments and in a competitive event at the World Robot Challenge 2018.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002