Robot Legs Mimic Human Walking Gait

Posted on July 6, 2012

Robot Legs With Human Walking Gait

Researchers from the University of Arizona have developed robotic legs that mimic the human walking gait. The researchers say their robotic set of legs are the very first to fully model walking in a biologically accurate manner. The researchers say the "neural architecture, musculoskeletal architecture and sensory feedback pathways in humans have been simplified and built into the robot." Take a look:

The researchers note that a key component of the human walking system is the central pattern generator (CPG). The CPG is a neural network in the lumbar region of the spinal cord that generates rhythmic muscle signals. The CPG produces, and then controls, these signals by gathering information from different parts of the body that are responding to the environment. It enables people to walk without having to think about it.

The robotic leg developers say the simplest form of a CPG is a half-centre, which consists of two neurons that fire signals alternatively, producing a rhythm. The robot legs contain an artificial half-centre as well as sensors that deliver information back to the half-centre, including load sensors that sense force in the limb when the leg is pressed against a stepping surface.

Dr. Theresa Klein, co-author of the study, says, "Interestingly, we were able to produce a walking gait, without balance, which mimicked human walking with only a simple half-centre controlling the hips and a set of reflex responses controlling the lower limb."

The University of Arizona researchers hypothesize that babies start off with a simple half-centre, similar to the one developed in this robot, and over time they "learn" a network for a more complex walking pattern. The researchers say this could explain why babies have been seen to exhibit a simple walking pattern when placed on a treadmill even before they have learned to walk - a simple half-centre is already in place.

The research paper about the robot legs was published here in IOP Publishing's Journal of Neural Engineering.

Image: University of Arizona