Detail publikace

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

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

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

5. 6. 2024

Nakladatel

MDPI

Místo

BASEL

ISSN

2079-3197

Periodikum

Computation

Ročník

12

Číslo

6

Stát

Švýcarská konfederace

Strany od

116

Strany do

132

Strany počet

17

URL

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

Plný text v Digitální knihovně

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"
}