Autonomous Manipulator of a Mobile Robot Based on a Vision System

Annusewicz-Mistal, Anna; Pietrala, Dawid; Łaski, P.; Zwierzchowski, Jaroslaw; Borkowski, Krzysztof; Bracha, Gabriel; Borycki, Kamil; Kostecki, Szczepan; Wlodarczyk, Daniel

doi:10.3390/app13010439

Cited by 3 publications

(1 citation statement)

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“…However, as the manipulator's degree of freedom increases, the complexity of the dynamic modeling and the number of inverse kinematics solution calculations increase, and the inverse solution is not singular. All these approaches have restrictions in terms of how they can be used [16,17]. Path-tracking issues can now be solved without the need for system-dynamics modeling thanks to a number of recent successful applications of reinforcement learning to decision-control problems.…”

Section: Introductionmentioning

confidence: 99%

A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning

Zhang

Kan

2023

Applied Sciences

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The continuous path of a manipulator is often discretized into a series of independent action poses during path tracking, and the inverse kinematic solution of the manipulator’s poses is computationally challenging and yields inconsistent results. This research suggests a manipulator-route-tracking method employing deep-reinforcement-learning techniques to deal with this problem. The method of this paper takes an end-to-end-learning approach for closed-loop control and eliminates the process of obtaining the inverse answer by converting the path-tracking task into a sequence-decision issue. This paper first explores the feasibility of deep reinforcement learning in tracking the path of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom (multi-DOF) manipulator was performed by combining the maximum-entropy deep-reinforcement-learning algorithm. The experimental findings demonstrate that the approach performs well in manipulator-path tracking, avoids the need for an inverse kinematic solution and a dynamics model, and is capable of performing manipulator-tracking control in continuous space. As a result, this paper proposes that the method presented is of great significance for research on manipulator-path tracking.

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Section: Introductionmentioning

confidence: 99%