Momentum Model-based Minimal Parameter Identification of a Space Robot

TL;DR

This paper introduces a novel momentum model-based method for minimal parameter identification of space robots on orbit, enabling accurate motion planning and control after launch by estimating inertial parameters from pose and twist data.

Contribution

It proposes a unique linear formulation of the momentum model and a joint trajectory optimization technique for minimal parameter estimation in space robots.

Findings

Effective identification on a 12-DOF dual-arm space robot

Method is scalable and requires only pose and twist data

Applicable to tree-type space robots for on-orbit parameter estimation

Abstract

Accurate information of inertial parameters is critical to motion planning and control of space robots. Before the launch, only a rudimentary estimate of the inertial parameters is available from experiments and computer-aided design (CAD) models. After the launch, on-orbit operations substantially alter the value of inertial parameters. In this work, we propose a new momentum model-based method for identifying the minimal parameters of a space robot while on orbit. Minimal parameters are combinations of the inertial parameters of the links and uniquely define the momentum and dynamic models. Consequently, they are sufficient for motion planning and control of both the satellite and robotic arms mounted on it. The key to the proposed framework is the unique formulation of momentum model in the linear form of minimal parameters. Further, to estimate the minimal parameters, we propose a novel joint trajectory planning and optimization technique based on direction combinations of joints' velocity. The efficacy of the identification framework is demonstrated on a 12 degrees-of-freedom, spatial, dual-arm space robot. The methodology is developed for tree-type space robots, requires just the pose and twist data, and scalable with increasing number of joints.