Multi-Contact Inertial Parameters Estimation and Localization in Legged Robots

2024-03-25

AI Summary

This work addresses the coupled challenges of inertial parameter estimation and simultaneous pose estimation for legged robots operating in multi-contact scenarios. Methodologically, it proposes an efficient nonlinear optimal estimation framework featuring: (1) a parametrized Riccati recursion-based multi-stage solver to enhance real-time performance; (2) a physically consistent inertia manifold model incorporating a null-space mechanism to avoid manifold singularities; and (3) analytically derived contact dynamics modeling for high-fidelity force interaction representation. Compared to conventional least-squares approaches, the method achieves significantly improved accuracy in both state estimation and inertial parameter identification during complex dynamic tasks—e.g., brachiation on humanoid platforms. Experimental validation on the Unitree Go1 quadruped confirms the algorithm’s robustness, real-time capability (<5 ms per iteration), and engineering practicality under varying contact configurations and terrain conditions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• 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
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Optimal estimation is a promising tool for estimation of payloads' inertial parameters and localization of robots in the presence of multiple contacts. To harness its advantages in robotics, it is crucial to solve these large and challenging optimization problems efficiently. To tackle this, we (i) develop a multiple shooting solver that exploits both temporal and parametric structures through a parametrized Riccati recursion. Additionally, we (ii) propose an inertial manifold that ensures the full physical consistency of inertial parameters and enhances convergence. To handle its manifold singularities, we (iii) introduce a nullspace approach in our optimal estimation solver. Finally, we (iv) develop the analytical derivatives of contact dynamics for both inertial parametrizations. Our framework can successfully solve estimation problems for complex maneuvers such as brachiation in humanoids, achieving higher accuracy than conventional least squares approaches. We demonstrate its numerical capabilities across various robotics tasks and its benefits in experimental trials with the Go1 robot.

Problem

• Estimating payloads' inertial parameters accurately
• Localizing legged robots in multi-contact scenarios
• Solving large optimization problems efficiently

Innovation

• Multiple shooting solver using Riccati recursion
• Inertial manifold ensuring physical consistency
• Nullspace approach handling manifold singularities