Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00328383" target="_blank" >RIV/68407700:21230/18:00328383 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8593374" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8593374</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS.2018.8593374" target="_blank" >10.1109/IROS.2018.8593374</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

Popis výsledku v původním jazyce

The effectiveness of the robot locomotion can be measured using the cost of transport (CoT) which represents the amount of energy that is needed for traversing from one place to another. Terrains excerpt different mechanical properties when crawled by a multi-legged robot, and thus different values of the CoT. It is therefore desirable to estimate the CoT in advance and plan the robot motion accordingly. However, the CoT might not be known prior the robot deployment, e.g., in extraterrestrial missions; hence, a robot has to learn different terrains as it crawls through the environment incrementally. In this work, we focus on estimating the CoT from visual and geometrical data of the crawled terrain. A thorough analysis of different terrain descriptors within the context of incremental learning is presented to select the best performing approach. We report on the achieved results and experimental verification of the selected approaches with a real hexapod robot crawling over six different terrains.

Název v anglickém jazyce

Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

Popis výsledku anglicky

The effectiveness of the robot locomotion can be measured using the cost of transport (CoT) which represents the amount of energy that is needed for traversing from one place to another. Terrains excerpt different mechanical properties when crawled by a multi-legged robot, and thus different values of the CoT. It is therefore desirable to estimate the CoT in advance and plan the robot motion accordingly. However, the CoT might not be known prior the robot deployment, e.g., in extraterrestrial missions; hence, a robot has to learn different terrains as it crawls through the environment incrementally. In this work, we focus on estimating the CoT from visual and geometrical data of the crawled terrain. A thorough analysis of different terrain descriptors within the context of incremental learning is presented to select the best performing approach. We report on the achieved results and experimental verification of the selected approaches with a real hexapod robot crawling over six different terrains.

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

<a href="/cs/project/GA18-18858S" target="_blank" >GA18-18858S: Metody kontinuálního učení řízení pohybu vícenohých kráčejích robotů v úlohách autonomního sběru dat</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN

978-1-5386-8094-0

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

6

Strana od-do

1745-1750

Název nakladatele

IEEE Press

Místo vydání

New York

Místo konání akce

Madrid

Datum konání akce

1. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458872701124