Motion Primitive Recognition on Human Guided Robotic Arm using Machine Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2019 19th International Conference on Control, Automation and Systems (ICCAS), 2020, 2019-October, pp. 955-960
- Issue Date:
- 2020
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 08971755.pdf | 1.37 MB |
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© 2019 Institute of Control, Robotics and Systems - ICROS. This paper proposed a novel intuitive teaching technologies by reconstructing the recorded motion information during human guided robotic arm. A learning algorithm is proposed in this paper to recognize the motion primitives according to therbligs definition. The hybrid sensing interface is used to record and modified the positional trajectory, force/torque, and gripper information. Furthermore, an extended Kalman filter is used to pre-process the data and estimate the velocity and acceleration profile as motion features. The motion features, output data via the hybrid sensing interface, is finally used to recognize the target therblig by proposed cascade support vector machine. The experimental results show that the proposed method can recognize the motion features into therbligs correctly and efficiently. The recognition results can be further used to reconstruct an assembly operation.
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