A mechanism of Particle Swarm Optimization on motor patterns in the {B4LC} system

This paper presents an optimization mechanism for feed-forward control units termed as motor patterns in the Bio-inspired Behavior-Based Bipedal Locomotion Control (B4LC) system. In specific locomotion phases, motor patterns ActiveHipSwing and LegPropel produce torques at the hip and ankle joints respectively. Biped is activated to swing leg ahead and push body forward by corresponding motor patterns, in which the parameters determine the profile of generated torques according to the sigmoid function. To obtain efficient and stable locomotion control, we employ Particle Swarm Optimization (PSO) method to tune motor patterns’ parameters by formulating locomotion stability, energy consumption and walking speed as fitness functions. The optimization procedure takes place on a 3-dimensional simulated bipedal robot. Simulation results prove that suggested approach reinforces the walking behavior of biped with respect to stability, velocity control as well as enhances the energy efficiency significantly.