ORCID
- Li, Chunxu: 0000-0001-7851-0260
Abstract
In this paper, a gradient neural network (GNN) is presented, analyzed and discussed to solve the time-varying inverse kinematics solution of the four-wheel mobile robotic arm, which can approximate the time varying inverse kinematics solution. A monolithic kinematics model of mobile robotic arm is established, and the inverse kinematics solution can synchronously coordinate the control of the mobile platform and the robotic arm to accomplish the task of the end-executor. Besides, the computer numerical results are provided to attest validity and high exactitude of GNN model in settling the time-varying inverse kinematics of a four-wheel mobile robotic arm.
DOI
10.1109/ddcls52934.2021.9455633
Publication Date
2021-05-14
Publication Title
2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)
First Page
391
Last Page
396
Embargo Period
2021-07-02
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Zhou, Y., Liu, K., Li, C., Wang, G., Liu, Y., & Sun, Z. (2021) 'A Gradient Neural Network for online Solving the Time-varying Inverse Kinematics Problem of Four-wheel Mobile Robotic Arm', 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), , pp. 391-396. Available at: https://doi.org/10.1109/ddcls52934.2021.9455633
