Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

With the aim of producing a stable human-like bipedal gait, a five-link planar walking mechanism was coupled with a central pattern generator (CPG) neural network, consisting of units based on Matsuoka half-center oscillator model with a firm basis in neurophysiologic studies. As a minimalistic approach to bipedal walking, this type of walking mechanism contains only four actuators, and is lacking feet and ankles. Firstly, the mechanism was fashioned as a computer simulation with realistic physics, providing a platform for heuristic tests and allowing accurate fitness evaluations for the creation of CPG controllers through evolutionary algorithms. The oscillatory characteristics of the CPG networks, their internal connectivity structure, and the external feedback pathways were subject to a genetic algorithms (GA) optimization. In the second stage, the evolved CPG networks were transferred to a hardware implementation of the mechanism, to test their performance under real-world dynamics. Results confirmed that the biologically inspired CPG model is very well suited for controlling legged locomotion, since a diverse manifestation of CPG networks (both with and without external feedback) have been observed to succeed during the course of GA evaluations. Observations also implied that while the CPG mechanism is inherently able to sustain a stable gait, the utilization of feedback pathways makes the gait more human-like and is needed to provide a means to adapt to irregularities in the environment.