A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

Publication: Research - peer-reviewArticle in proceedings

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Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior
Original languageEnglish
Title of host publicationProceedings of the First International Symposium on Swarm Behavior and Bio-Inspired Robotics
Number of pages4
PublisherSWARM
Publication date2016
StatePublished - 2016
Event1st International Symposium on Swarm Behavior and Bio-Inspired Robotics - Kyoto, Japan

Conference

Conference1st International Symposium on Swarm Behavior and Bio-Inspired Robotics
Nummer1
LandJapan
ByKyoto
Periode26/10/201528/10/2015

Bibliographical note

Published on USB

    Keywords

  • Biped robot, Neural control, Locomotion control, Embodied learning, Legged robots