Vertical Stabilization of Bipedal Walking Drone PAVO with Proximal Policy Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0200912" target="_blank" >RIV/00216305:26210/26:0200912 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ME61309.2024.10789752" target="_blank" >http://dx.doi.org/10.1109/ME61309.2024.10789752</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ME61309.2024.10789752" target="_blank" >10.1109/ME61309.2024.10789752</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Vertical Stabilization of Bipedal Walking Drone PAVO with Proximal Policy Optimization

Popis výsledku v původním jazyce

While autonomous mobile robots have gained more popularity over the last few years and many traditional problems (such as navigation and locomotion) seem to have been solved with an adequate level of accuracy, walking and biped robots still face many challenges. We present an approach to the stabilization of mobile robots with two legs, which move via quasi-dynamic movement. A popular machine learning method, Proximal Policy Optimization (PPO), was used to learn how to stabilize the robot in a vertical position. This method is popular for solving complex problem domains with high-dimensional state-action spaces and continuous states and actions, which are common in areas involving walking robots with a high number of degrees of freedom. The method was tested on a custom-designed biped walking robot, PAVO.

Název v anglickém jazyce

Vertical Stabilization of Bipedal Walking Drone PAVO with Proximal Policy Optimization

Popis výsledku anglicky

While autonomous mobile robots have gained more popularity over the last few years and many traditional problems (such as navigation and locomotion) seem to have been solved with an adequate level of accuracy, walking and biped robots still face many challenges. We present an approach to the stabilization of mobile robots with two legs, which move via quasi-dynamic movement. A popular machine learning method, Proximal Policy Optimization (PPO), was used to learn how to stabilize the robot in a vertical position. This method is popular for solving complex problem domains with high-dimensional state-action spaces and continuous states and actions, which are common in areas involving walking robots with a high number of degrees of freedom. The method was tested on a custom-designed biped walking robot, PAVO.

Druh

D - Stať ve sborníku

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 statě ve sborníku

Proceedings of the 2024 21st International Conference on Mechatronics - Mechatronika, ME 2024

ISBN

979-8-3503-9491-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

—

Místo vydání

—

Místo konání akce

Brno

Datum konání akce

4. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001414274500001