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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU151852" target="_blank" >RIV/00216305:26210/24:PU151852 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2079-3197/12/6/116" target="_blank" >https://www.mdpi.com/2079-3197/12/6/116</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/computation12060116" target="_blank" >10.3390/computation12060116</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computation

ISSN

2079-3197

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

„116“-„“

UT code for WoS article

001254584000001

EID of the result in the Scopus database

2-s2.0-85196774480