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Article


Title

Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm

Authors

[ 1 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.8] Mechanical engineering

Year of publication

2022

Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 13

Article type

scientific article

Publication language

english

Keywords
EN
  • obstacle avoidance
  • positioning
  • robotic arm
  • reinforcement learning
Abstract

EN In this paper, the application of the policy gradient Reinforcement Learning-based (RL) method for obstacle avoidance is proposed. This method was successfully used to control the movements of a robot using trial-and-error interactions with its environment. In this paper, an approach based on a Deep Deterministic Policy Gradient (DDPG) algorithm combined with a Hindsight Experience Replay (HER) algorithm for avoiding obstacles has been investigated. In order to ensure that the robot avoids obstacles and reaches the desired position as quickly and as accurately as possible, a special approach to the training and architecture of two RL agents working simultaneously was proposed. The implementation of this RL-based approach was first implemented in a simulation environment, which was used to control the 6-axis robot simulation model. Then, the same algorithm was used to control a real 6-DOF (degrees of freedom) robot. The results obtained in the simulation were compared with results obtained in laboratory conditions.

Date of online publication

30.06.2022

Pages (from - to)

6629-1 - 6629-24

DOI

10.3390/app12136629

URL

https://www.mdpi.com/2076-3417/12/13/6629

Comments

Article Number: 6629

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Points of MNiSW / journal

100.0

Impact Factor

2.679 [List 2020]

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