A neural network with central pattern generators entrained by sensory feedback controls walking of a bipedal model

Abstract

A neuromechanical simulation of a planar, bipedal walking robot has been developed. It is constructed as a simplified musculoskeletal system to mimic the biomechanics of the human lower body. The controller consists of a dynamic neural network with central pattern generators (CPGs) entrained by force and movement sensory feedback to generate appropriate muscle forces for walking. The CPG model is a two-level architecture, which consists of separate rhythm generator (RG) and pattern formation (PF) networks. The presented planar biped model walks stably in the sagittal plane without inertial sensors or a centralized posture controller or a “baby walker” to help overcome gravity. Its gait is similar to humans’ with a walking speed of 1.2 m/s. The model walks over small obstacles (5% of the leg length) and up and down 5° slopes without any additional higher level control actions.