1. A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology
- Author
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Zhang Feng, Shen Tian, Bai Ning, Hongwei Wang, Long Cui, Nailong Liu, and Zhaoming Liu
- Subjects
0303 health sciences ,Modularity (networks) ,Article Subject ,General Computer Science ,business.industry ,Computer science ,Type (model theory) ,Motion control ,Motion (physics) ,law.invention ,03 medical and health sciences ,Industrial robot ,0302 clinical medicine ,Control and Systems Engineering ,law ,Component (UML) ,TJ1-1570 ,Trajectory ,Higher animals ,Computer vision ,Mechanical engineering and machinery ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.
- Published
- 2020