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A New Kinect V2-Based Method for Visual Recognition and Grasping of a Yarn-Bobbin-Handling Robot
Jinghai Han1, Bo Liu2, Yongle Jia2
1Institute of Rail Transport, Nanjing Vocational Institute of Transport Technology, Nanjing 211188, China.
Micromachines
|June 24, 2022
Summary
This study introduces a Kinect V2-based robotic system for yarn bobbin grasping, reducing human dependency. The visual method achieves over 80% success rate and operates within 10 seconds, meeting industrial needs.
Area of Science:
- Robotics
- Computer Vision
- Industrial Automation
Background:
- Human intervention is often required for yarn bobbin grasping in robotic systems.
- Existing methods may lack precision and efficiency in automated grasping operations.
Purpose of the Study:
- To develop a Kinect V2-based visual servoing method for autonomous yarn bobbin grasping.
- To reduce human dependence and improve the efficiency of robotic yarn bobbin handling.
Main Methods:
- Utilized Kinect V2 for 3D point cloud data acquisition of yarn bobbins.
- Applied noise filtering, M-estimator sample consensus (MSAC) for plane fitting, Principal Component Analysis (PCA) for initial registration, and Iterative Closest Point (ICP) for precise pose estimation.
Main Results:
- The proposed method achieved an average working time of under 10 seconds.
- Demonstrated a grasping success rate exceeding 80% in various scenarios.
- Validated the system's performance on an experimental platform.
Conclusions:
- The Kinect V2-based visual method effectively enables precise yarn bobbin pose determination for robotic grasping.
- The system meets industrial production requirements for speed and accuracy.
- This approach significantly reduces the need for human intervention in the grasping process.
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