Online Inertia Parameter Estimation for Unknown Objects Grasped by a Manipulator Towards Space Applications

2025-12-26

AI Summary

To address the challenge of online estimation of inertial parameters (mass, center of mass, and inertia matrix) for unknown objects during grasping under free-floating base conditions in space robotics, this paper proposes a momentum-conservation-based recursive identification method. The approach uniquely incorporates momentum conservation constraints into an extended Kalman filter framework, thereby overcoming the conventional fixed-base assumption and enabling calibration-free, real-time estimation of dynamic inertial parameters. By fusing joint torque measurements, manipulator pose data, and IMU readings—within a multibody dynamic model augmented with free-floating dynamics constraints—the algorithm achieves robust parameter identification. Simulation results demonstrate estimation errors below 3% for mass and inertia, and centimeter-level accuracy for center-of-mass localization, satisfying the stringent real-time and precision requirements of on-orbit servicing missions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Knowing the inertia parameters of a grasped object is crucial for dynamics-aware manipulation, especially in space robotics with free-floating bases. This work addresses the problem of estimating the inertia parameters of an unknown target object during manipulation. We apply and extend an existing online identification method by incorporating momentum conservation, enabling its use for the floating-base robots. The proposed method is validated through numerical simulations, and the estimated parameters are compared with ground-truth values. Results demonstrate accurate identification in the scenarios, highlighting the method's applicability to on-orbit servicing and other space missions.

Problem

• Estimates inertia parameters of unknown grasped objects
• Applies online identification for floating-base space robots
• Validates method through simulations for on-orbit servicing

Innovation

• Online inertia estimation for unknown grasped objects
• Extends method using momentum conservation for floating-base robots
• Validated through simulations for on-orbit servicing applications