Publication detail

Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures

PARÁK, R. KŮDELA, J. MATOUŠEK, R. JUŘÍČEK, M.

Original Title

Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures

Type

journal article in Web of Science

Language

English

Original Abstract

The use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the utilization of artificial intelligence techniques. The use of these technologies may be limited due to a focus on a specific type of robotic manipulator and a particular solved task, which can hinder modularity and reproducibility in future expansions. This paper presents a method for planning motion across a wide range of robotic structures using deep reinforcement learning (DRL) algorithms to solve the problem of reaching a static or random target within a pre-defined configuration space. The paper addresses the challenge of motion planning in environments under a variety of conditions, including environments with and without the presence of collision objects. It highlights the versatility and potential for future expansion through the integration of OpenAI Gym and the PyBullet physics-based simulator.

Keywords

deep reinforcement learning; motion planning; collision avoidance; physics-based simulation; industrial robotics

Authors

PARÁK, R.; KŮDELA, J.; MATOUŠEK, R.; JUŘÍČEK, M.

Released

5. 6. 2024

Publisher

MDPI

Location

BASEL

ISBN

2079-3197

Periodical

Computation

Year of study

12

Number

6

State

Swiss Confederation

Pages from

116

Pages to

132

Pages count

17

URL

https://www.mdpi.com/2079-3197/12/6/116

Full text in the Digital Library

http://hdl.handle.net/11012/249511

BibTex

@article{BUT189243,
  author="Roman {Parák} and Jakub {Kůdela} and Radomil {Matoušek} and Martin {Juříček}",
  title="Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures",
  journal="Computation",
  year="2024",
  volume="12",
  number="6",
  pages="17",
  doi="10.3390/computation12060116",
  issn="2079-3197",
  url="https://www.mdpi.com/2079-3197/12/6/116"
}