Experimental Leg Inverse Dynamics Learning of Multi-legged Walking Robot

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00355325" target="_blank" >RIV/68407700:21230/21:00355325 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-70740-8_10" target="_blank" >https://doi.org/10.1007/978-3-030-70740-8_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-70740-8_10" target="_blank" >10.1007/978-3-030-70740-8_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Experimental Leg Inverse Dynamics Learning of Multi-legged Walking Robot

Popis výsledku v původním jazyce

Rough terrain locomotion is a domain where multi-legged robots benefit from their relatively complex morphology compared to the wheeled or tracked robots. Efficient rough terrain locomotion requires the legged robot sense contacts with the terrain to adapt its behavior and cope with the terrain irregularities. Usage of inverse dynamics to estimate the leg state and detect the leg contacts with the terrain suffers from computational complexity. Furthermore, it requires a precise analytical model identification that does not cope with adverse changes of the leg parameters such as friction changes due to the joint wear, the increased weight of the leg due to the mud deposits, and possible leg morphology change due to damage. In this paper, we report the experimental study on the locomotion performance with machine learning-based inverse dynamics model learning. Experimental examining three different learning models show that a simplified model is sufficient for leg collision detection learning. Moreover, the learned model is faster for calculation and generalizes better than more complex models when the leg parameters change.

Název v anglickém jazyce

Experimental Leg Inverse Dynamics Learning of Multi-legged Walking Robot

Popis výsledku anglicky

Rough terrain locomotion is a domain where multi-legged robots benefit from their relatively complex morphology compared to the wheeled or tracked robots. Efficient rough terrain locomotion requires the legged robot sense contacts with the terrain to adapt its behavior and cope with the terrain irregularities. Usage of inverse dynamics to estimate the leg state and detect the leg contacts with the terrain suffers from computational complexity. Furthermore, it requires a precise analytical model identification that does not cope with adverse changes of the leg parameters such as friction changes due to the joint wear, the increased weight of the leg due to the mud deposits, and possible leg morphology change due to damage. In this paper, we report the experimental study on the locomotion performance with machine learning-based inverse dynamics model learning. Experimental examining three different learning models show that a simplified model is sufficient for leg collision detection learning. Moreover, the learned model is faster for calculation and generalizes better than more complex models when the leg parameters change.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Modelling and Simulation for Autonomous Systems

ISBN

978-3-030-70739-2

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

154-168

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Praha

Datum konání akce

21. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000763018100010