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 is coupled with a central pattern generator (CPG) neural network, based on Matsuoka's half-center oscillator model with a basis in neurophysiology. As a minimalistic approach to bipedal walking, this mechanism contains four actuators, and is lacking feet and ankles. The mechanism is simulated with accurate physics, allowing realistic fitness evaluations for the creation of CPG controllers through evolutionary computation. The oscillatory parameters, connectivity structure, and feedback pathways of the networks are determined through genetic algorithms (GA) optimization. The evolved CPG networks are transferred to a hardware implementation, to test their performance under real-world dynamics. Results confirm that the biologically inspired CPG model is well suited for controlling legged locomotion, since a diverse manifestation of CPG networks have been observed to succeed during the course of GA evaluations. While the CPG mechanism is inherently able to sustain a stable gait, the utilization of feedback makes the gait more human-like and adaptive to irregularities in the environment.