Dynamic Balance Control for Biped Robot Walking Using Sensor Fusion, Kalman Filter, and Fuzzy Logic

Abstract

The walking ability is a fundamental research for biped robots, and there always needs to be a criterion used for judging the walking stability. The zero moment point (ZMP) criterion is one of the useful standards for measuring biped robot walking. Here, a new ZMP trajectory model with adjustable parameters is proposed to modulate the ZMP trajectory both in sagittal and lateral planes and make the ZMP trajectory more flexible. A dynamic balance control (DBC), which includes Kalman filter (KF) and the fuzzy motion controller (FMC), is also designed to keep the body balance and make the biped walking following the desired ZMP reference. In addition, KF is utilized to estimate the system states and reduce the effect caused by noise. Using sensor fusion technique, ZMP error and trunk inclination measured by the force sensor and accelerometer are served as the inputs for FMC, which is presented to correct each joint of the biped robot dynamically. When a biped robot walks under different ground conditions, the coordination of the designed ZMP trajectory and proposed DBC can achieve a successful biped walking. Finally, there are several experiments presented to demonstrate the feasibility and effectiveness of the proposed control scheme.