Grasping complex shapes with the integration of high-speed vision and machine learning in a dynamic situation
- Resource Type
- Conference
- Authors
- Kawahara, Hiromichi; Senoo, Taku; Ishii, Idaku
- Source
- 2023 IEEE International Conference on Mechatronics (ICM) Mechatronics (ICM), 2023 IEEE International Conference on. :1-6 Mar, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Target tracking
Mechatronics
Limiting
Shape
Machine vision
Computational modeling
Estimation
- Language
It is necessary to grasp a complex object with a certain level of speed and accuracy to substitute humans with robots in some studies. A system using machine learning can recognize complex objects, but its computation cost is so high such that it cannot grasp a moving object. In contrast, a system using high-frame-rate (HFR) vision can recognize objects quickly but cannot recognize complex shapes. In this study, we constructed a system combining HFR vision and machine learning to compensate for each other and attempt high-speed grasping for objects with complex shapes.