Li Yanqiu, Liu Huan, Gao Hailong
School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.
Department of Basic Sciences, Jilin Jianzhu University, Changchun, China.
Front Neurorobot. 2024 Jul 15;18:1431034. doi: 10.3389/fnbot.2024.1431034. eCollection 2024.
Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi-Sugeno-Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.
冗余机械手在众多领域被广泛应用以节省人力并提高工作效率。然而，冗余性使得机械手的逆运动学难以解决，从而增加了指导机械手执行给定任务的难度。首先，提出了一种基于在线学习回声状态网络和高木-菅野-康模糊推理系统（FIS）的在线学习模糊回声状态网络（OLFESN）来解决这个问题。然后，设计了一种基于OLFESN的控制方案来实现对冗余机械手的高效控制。此外，针对UR5和Franka Emika Panda机械手等冗余机械手进行了仿真和实验，以验证所提出控制方案的有效性。
