Fuzzy dynamic gait pattern generation for real-time push recovery control of a teen-sized humanoid robot

Abstract

A fuzzy dynamic gait pattern generator, which allows a teen-sized humanoid robot to generate, in real-time, a suitable gait pattern when it is hit by an unexpected force, is proposed in this paper. Conventional gait pattern generators usually utilize the ideal Zero Moment Point (ZMP) to plan the trajectory of the Center of Mass (CoM), along with a cycloid to generate steps. However, pre-planned gait patterns cannot deal with unexpected situations, especially instances when the robot experiences an unknown force. Therefore, we propose a dynamic gait pattern generator that leverages the Virtual Force Linear Inverted Pendulum Model (VFLIPM) to adjust the trajectory of the CoM, and which detects balance status by estimating the trajectory of the ZMP using eight high-precision load cell pressure sensors mounted onto the robot's soles. We integrate an accelerometer and the pressure sensors through a fuzzy controller to instantly respond to external forces and generate a suitable gait pattern. When the robot is pushed suddenly, it first adopts a pre-planned gait pattern to replace the current gait. At the same time, the fuzzy controller calculates the recovery gait, with appropriate strides and lean angles to absorb the impact. The proposed method is implemented on the teen-sized humanoid robot, David Junior II, for the Push Recovery event at RoboCup. David Junior II endured a hit with potential energy during walking, which is 1.3 times more robust than when standing.