Autonomous Real–Time Mass Center Location and Inertia Identification for Grappling Space Robotics

Abstract

Grappling actions by space robots for the purposes of stabilizing, refueling, repair, and equipment replacement necessitate autonomous abilities of a single grappling space robot to rapidly contend with large variations in total system inertia rapidly shifting system center of mass, as targets can be massive with possibly unknown or poorly known mass inertia properties. Proposing time–varying inertia identification yields opportunities for novel online calculation of time–varying location of the combined system’s center of mass. Two–norm optimal nonlinear, projection regression–based learning is implemented and juxtaposed to a comparative benchmark both qualitatively and quantitatively supported by a comparison of enhancements of Luenberger observers. Following analytical development, simulations are used to verify the design, and then spaceflight experiments are proposed for the sequel to validate the simulation results. Time–varying mass locations are discerned, and the time–varying location of the mass center is revealed to be 36–95 percent different than initially assumed, and 58–317 percent corrections to inertia identification is demonstrated. Combined three–dimensional maneuvers obscure identification compared to single–axis maneuvering.