Optimal robot dynamics local identification using genetic-based path planning in workspace subregions

Abstract

Methods for dynamic calibrations of Industrial Robots (IR) are increasing their importance in many applications because of high performances attained by model-based control strategies. Most of known state-of-the-art methods aim at modeling robots along the complete workspace, often affecting the identified parameters with loss of physical meaning (e.g. negative inertia values) and requiring a wide exploration of the workspace both in term of joint positions and velocities (accelerations). Actually, many IR tasks require dynamic accuracy in limited portion of the workspace and commonly display mild dynamics. Local identification of dynamics parameters in task conditions could therefore increase the predictive capability of the model for that operation. This work proposes the use of a parametric-description of trajectories in Cartesian space, corresponding to the standard industrial path-description as a series of via-points in most of programming languages. The identification of the optimal exciting Cartesian trajectory in a local sub-region of the workspace is made by a genetic algorithm over the template trajectory description. The use of an IR real interpolator allows to match computational and task execution conditions.