Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336138" target="_blank" >RIV/68407700:21230/19:00336138 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1088/1748-3190/ab1a9c" target="_blank" >https://doi.org/10.1088/1748-3190/ab1a9c</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1748-3190/ab1a9c" target="_blank" >10.1088/1748-3190/ab1a9c</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot

Popis výsledku v původním jazyce

In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration with motion control is an unexplored topic. In our work, we propose to utilize the LGMD neural network for visual interception detection with a central pattern generator (CPG) for locomotion control of a hexapod walking robot that are combined in the controller based on the long short-term memory (LSTM) recurrent neural network. Moreover, we propose self-supervised learning of the integrated controller to autonomously find a suitable setting of the system using a realistic robotic simulator. Thus, individual neural networks are trained in a simulation to enhance the performance of the controller that is then experimentally verified with a real hexapod walking robot in both collision and interception avoidance scenario and navigation in a cluttered environment.

Název v anglickém jazyce

Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot

Popis výsledku anglicky

In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration with motion control is an unexplored topic. In our work, we propose to utilize the LGMD neural network for visual interception detection with a central pattern generator (CPG) for locomotion control of a hexapod walking robot that are combined in the controller based on the long short-term memory (LSTM) recurrent neural network. Moreover, we propose self-supervised learning of the integrated controller to autonomously find a suitable setting of the system using a realistic robotic simulator. Thus, individual neural networks are trained in a simulation to enhance the performance of the controller that is then experimentally verified with a real hexapod walking robot in both collision and interception avoidance scenario and navigation in a cluttered environment.

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

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

2019

Kód důvěrnosti údajů

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

Název periodika

Bioinspiration & Biomimetics

ISSN

1748-3182

e-ISSN

1748-3190

Svazek periodika

14

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

PT - Portugalská republika

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000509126400002

EID výsledku v databázi Scopus

2-s2.0-85065598130