Optimized System Identification of Humanoid Robots with Physical Consistency Constraints and Floating-based Exciting Motions

Abstract

Generally, robots are initially designed via computer-aided software to obtain their parameters. However, the given parameters are not entirely true due to their unmodeled parts; therefore, system identification is needed, and physical conditions are crucial to guarantee feasible solutions. This study uses a quadratic programming regression model accompanied by physical consistency constraints and designs specific exciting motions for humanoid robots. The proposed constraints are designed based on the geometric approximation of link objects, the physically reasonable mass and inertia and the total mass of the robot being correct. The proposed exciting motions include the general walking motion and the single-leg support motion, which enable a more flexible and stable way to cause excitation in the floating-base system. The identified parameters are evaluated on the humanoid robot NINO. Furthermore, the error between the feedback information of the zero moment point and the command information of the center of mass are used for evaluating the identified dynamic parameters. According to the experiments with the proposed exciting motions, the identified parameters are found to be obviously better than the original computer-aided design parameters, especially in the [Formula: see text] direction.