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Online learning fuzzy echo state network with applications on redundant manipulators.

Yanqiu Li¹, Huan Liu¹, Hailong Gao²

¹School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.

Frontiers in Neurorobotics

|August 1, 2024

Summary

This study introduces an online learning fuzzy echo state network (OLFESN) to solve inverse kinematics for redundant manipulators, enhancing control efficiency. Experiments confirm the OLFESN

Keywords:

echo state network (ESN)fuzzy inference system (FIS)online learning optimization redundant manipulators

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Area of Science:

Robotics and Control Systems
Artificial Intelligence
Machine Learning

Background:

Redundant manipulators are crucial for efficiency but pose challenges in inverse kinematics.
Controlling redundant manipulators requires sophisticated methods to handle their complex joint configurations.

Purpose of the Study:

To develop an effective control scheme for redundant manipulators using advanced AI.
To address the inverse kinematics problem in redundant manipulator control.

Main Methods:

Proposed an online learning fuzzy echo state network (OLFESN).
OLFESN integrates an online learning echo state network with a Takagi-Sugeno-Kang fuzzy inference system (FIS).
Devised an OLFESN-based control scheme for redundant manipulators.

Main Results:

The OLFESN-based control scheme demonstrated effective control of redundant manipulators.
Simulations and experiments validated the proposed method on UR5 and Franka Emika Panda robots.

Conclusions:

The OLFESN provides a viable solution for the inverse kinematics of redundant manipulators.
The proposed control scheme significantly improves the efficiency and instruction of redundant manipulators.