Evolutionary-Optimized Central Pattern Generator for Stable Modifiable Bipedal Walking

In this paper, an evolutionary-optimized central pattern generator (CPG) considering equality constraints is proposed for stable modifiable bipedal walking. The proposed CPG generates the position trajectories of the swing foot and the center of pelvis in the Cartesian coordinate system at single and double support phases. The significance of the proposed CPG is that it can change the sagittal and lateral step lengths just before the beginning of each single support phase while maintaining the desired values of single and double support times, which are set in the beginning of bipedal walking. To deal with environmental perturbations, the sensory feedbacks in the CPG are designed using the force sensing resistors such that the bipedal robot can maintain its balance. For the optimized parameters of the CPG, a two-phase evolutionary programming is employed. The effectiveness of the method is demonstrated by computer simulation with the Webots model of a small-sized humanoid robot, HSR-IX, and the experiment with HSR-IX developed in the RIT Laboratory, KAIST, Daejeon, Korea.