Machine Learning Algorithm for Anthropomorphic Manipulator Control System

DOI

10.2991/aisr.k.201029.066How to use a DOI?

Keywords

anthropomorphic manipulator, forward kinematics, artificial neural network, machine learning, deep reinforcement learning

Abstract

Service robots are one of the relevant areas of modern robotics. Many service robots are equipped with a pair of anthropomorphic manipulators, so that they are able to perform complex operations. However, this approach leads to new challenges in development of the robot control systems. In this paper we propose an algorithm for training the control system of two anthropomorphic manipulators with 7 degrees of mobility having intersecting work areas. The algorithm is based on deep reinforcement learning approach applied to the artificial neural network (ANN). The paper also describes the practical implementation of the ANN-based manipulator control system that avoids collisions and achieves an average accuracy of reproducing target positions of manipulator end effector of 98.3%. The ANN training was carried out using Keras framework. The obtained results indicate the promise of applying the proposed method for the development of control systems for anthropomorphic manipulators based on deep reinforcement learning.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Vyacheslav Petrenko
AU  - Fariza Tebueva
AU  - Nikolay Svistunov
AU  - Andrey Pavlov
PY  - 2020
DA  - 2020/11/10
TI  - Machine Learning Algorithm for Anthropomorphic Manipulator Control System
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 353
EP  - 358
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.066
DO  - 10.2991/aisr.k.201029.066
ID  - Petrenko2020
ER  -