A Robot Motion Learning Method Using Broad Learning System Verified by Small-Scale Fish-Like Robot
提出一种基于宽度学习系统的机器人点到达运动控制策略，无需详细动力学建模，通过小尺度磁控仿生鱼实验验证了轨迹收敛与避障效果。
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.