GA BASED SELF-ORGANIZED STABLE HUMANOID ROBOT WALKING PATTERN GENERATORS DESIGN

This paper describes the design of CPGs for stable humanoid bipedal locomotion, using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators. Corresponding joint's neurons are connected by strength weight, to achieve more natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on ZMP and global attitude of the robot. In the algorthms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walk in the given environment and a simulation shows the results.