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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computation

ISSN

2079-3197

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

„116“-„“

Kód UT WoS článku

001254584000001

EID výsledku v databázi Scopus

2-s2.0-85196774480