Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions
- DOI
- 10.2991/aisr.k.201029.065How to use a DOI?
- Keywords
- modular robotic, reinforcement learning, path planning, multi-agent systems, Q-learning
- Abstract
Modular robots, consisting of many identical modules, are one of the most difficult areas of robotics. Each newly added element changes the shape and capabilities of the end device, for example, adds functionality or allows the robot to move in new planes. The reconfiguration of the kinematic structure is a sequence of movements of each robot module from the initial position of the initial configuration to the final position of the desired configuration. The paper considers a method for reconfiguration the kinematic structure of a mechatronic-modular robot using reinforcement learning. The proposed method will be built on the basis of a learning algorithm, where the information for training will be the actions taken and the “reward” is a value characterizing the quality of the robot’s completion of the target task. The purpose of the training is to build a control algorithm that maximizes the total reward for a certain period of time. The effectiveness of the learning algorithm was tested by computer simulation of a robot, consisting of 5, 10 and 15 modules, in the formation of the target configuration.
- 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/).
Cite this article
TY - CONF AU - Vyacheslav Petrenko AU - Mikhail Gurchinsky AU - Fariza Tebueva AU - Andrey Pavlov PY - 2020 DA - 2020/11/10 TI - Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 347 EP - 352 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.065 DO - 10.2991/aisr.k.201029.065 ID - Petrenko2020 ER -