Full-body joint trajectory generation using an evolutionary central pattern generator for stable bipedal walking

Abstract

Central pattern generator (CPG) is used to control the locomotion of vertebrate and invertebrate animals, such as walking, running or swimming. It consists of biological neural networks that can produce coordinated rhythmic signals by using simple input signals. In this paper, a full-body joint trajectory generator is proposed for stable bipedal walking by using an evolutionary optimized CPG. Sensory feedback pathways are proposed in the CPG structure, which uses force sensing resistor (FSR) signals. In order to optimize the parameters of CPG, quantum-inspired evolutionary algorithm is employed. Then, controller is developed to control the position of both ankles and pelvis and the pitching angles of shoulders. The proposed trajectory generator controls the position of the center of pelvis along lateral direction, and the pitching angle of both shoulders in addition to the position of both ankles for stable biped locomotion. The stability of biped locomotion along lateral direction is improved by controlling the position of the center of pelvis along lateral direction. To reduce yawing momentum, the pitching angle of both shoulders are controlled. The effectiveness is demonstrated by simulations with the Webot model of a small-sized humanoid robot, HSR-IX and real experiments with HSR-IX.