ICRA 2012 Paper Abstract

Close

Paper TuD02.4

Klein, Theresa (University of Arizona), Lewis, M. Anthony (University of Arizona)

A Neurorobotic Model of Bipedal Locomotion Based on Principles of Human Neuromuscular Architecture

Scheduled for presentation during the Regular Session "Humanoid Motion Planning and Control" (TuD02), Tuesday, May 15, 2012, 17:15−17:30, Meeting Room 2 (Chief Red Wing)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Neurorobotics, Biologically-Inspired Robots, Humanoid and Bipedal Locomotion

Abstract

In this paper, we present a walking biped, based on principles of mammalian neuromuscular architecture. Walking in mammals is a fluid, dynamical interaction between a central pattern generator, the biomechanics of the body, the environment, and sensory feedback. Our robot is designed based on principles of human leg muscle architecture. We incorporate load detecting force sensors that model Golgi tendon organs in the muscles, as well as foot pressure and joint angle sensors. These sensory feedback sources model those available in the human body. The robot is controlled by a spiking neuron simulation that integrates centrally generated (CPG) with peripheral (reflexive) responses. Using recent understanding of the neurobiology of locomotion, we are able to generate an effective and stable walking pattern using interactions between the biomechanics, CPG, and reflexive responses. The CPG drives overall limb motion at the hips, while phase modulated reflexive responses adapt the pattern of the lower limb to the needs of the step cycle. Load detection by the force sensors in the limb generates propulsive stepping, and controls entrainment of the CPG through positive force feedback. These concepts are important ones for locomotion in mammals that should be considered by roboticists developing walking robots.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-12-13  22:03:56 PST  Terms of use