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A Robot Motion Learning Method Using Broad Learning System Verified by Small-Scale Fish-Like Robot
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AI
DOI：10.1109/TCYB.2023.3269773.png)
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Abstract
En 中文
The widespread application of learning-based methods in robotics has allowed significant simplifications to controller design and parameter adjustment. In this article, robot motion is controlled with learning-based methods. A control policy using a broad learning system (BLS) for robot point-reaching motion is developed. A sample application based on a magnetic small-scale robotic system is designed without detailed mathematical modeling of the dynamic systems. The parameter constraints of the nodes in the BLS-based controller are derived based on Lyapunov theory. The design and control training processes for a small-scale magnetic fish motion are presented. Finally, the effectiveness of the proposed method is demonstrated by convergence of the artificial magnetic fish motion to the targeted area with the BLS trajectory, successfully avoiding obstacles.
Keywords:
Robots
Learning systems
Robot motion
Motion control
Laboratories
Service robots
Trajectory
Broad learning system (BLS)
learning from demonstration (LfD)
motion control
small-scale robot
stability analysis
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W