Research Article
An Enhanced GRU Model With Application to Manipulator Trajectory Tracking
@ARTICLE{10.4108/airo.v1i.7, author={Zuyan Chen and Jared Walters and Gang Xiao and Shuai Li}, title={An Enhanced GRU Model With Application to Manipulator Trajectory Tracking}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={1}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2022}, month={1}, keywords={Trajectory tracking, gated recurrent unit (GRU), neural hidden state, gate unit, robot manipulators}, doi={10.4108/airo.v1i.7} }- Zuyan Chen
Jared Walters
Gang Xiao
Shuai Li
Year: 2022
An Enhanced GRU Model With Application to Manipulator Trajectory Tracking
AIRO
EAI
DOI: 10.4108/airo.v1i.7
Abstract
Service robots, e.g. massage robots, have attracted more and more attention in recent years and the most popular study within this field is trajectory tracking. Due to the actual demand for service robots, the solution of trajectory tracking requires fast convergence and high accuracy. In order to solve the above issues, this paper proposed an enhanced Gated recurrent unit (GRU) to deal with trajectory tracking tasks of robot manipulators. The main feature of enhanced GRU is utilizing cell states as well as various gate units to build a novel neural cell. Besides, the presented enhanced GRU resolves the problem of the general neural network model and large memory occupancy. Then the derivations about the computational process of cell state and mixed hidden state of the proposed model have been illustrated. Finally, three trajectory tracking applications, comparison, and visual simulation have verified feasibility as well as the superiority of the enhanced GRU model.
Copyright © 2022 Zuyan Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
