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A Research on Manipulator Path Tracking based on Deep Reinforcement Learning
Version 1
: Received: 24 May 2023 / Approved: 26 May 2023 / Online: 26 May 2023 (05:24:45 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, P.; Zhang, J.; Kan, J. A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning. Appl. Sci. 2023, 13, 7867. Zhang, P.; Zhang, J.; Kan, J. A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning. Appl. Sci. 2023, 13, 7867.
Abstract
We propose a deep reinforcement learning based manipulator path tracking method to solve the computationally difficult and non-unique problem of manipulator path tracking methods based on inverse kinematics. By transforming the path tracking task into a sequence decision problem, our method adopts an end-to-end learning method for closed-loop control and avoids the process of finding the inverse solution. We first explored the feasibility of the deep reinforcement learning method in the path tracking of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom(multi-DOF) manipulator was realized by combining the maximum entropy deep reinforcement learning algorithm. The experimental results show that our method has a good effect on the path tracking of the manipulator, which not only avoids the process of finding the inverse kinematics solution, but also requires no dynamic model. Therefore, we believe that our method has great significance in the study of manipulator path tracking.
Keywords
path tracking; deep reinforcement learning; maximum entropy; inverse kinematics
Subject
Computer Science and Mathematics, Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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