Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion

2025-04-24

AI Summary

Online collision detection and external force estimation for quadrupedal robots during dynamic running remain challenging when relying solely on joint encoders and dynamics models, especially without prior gait assumptions. Method: This paper proposes the IMM-KF (Interacting Multiple Model–Kalman Filter) unified framework—the first to jointly estimate multimodal contact states and external forces within a single filtering structure. It operates without predefined gaits, enabling real-time responsiveness across arbitrary motion patterns. The framework integrates pseudo-measurement-based force estimation, reflexive swing-leg planning, admittance control, and force-adaptive model predictive control (MPC). Results: The method achieves millisecond-level collision detection and closed-loop force regulation. Simulation and hardware experiments demonstrate an external force estimation RMSE of <1.2 N, significantly enhancing robustness and stability during high-speed locomotion over complex terrain.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

In this paper we address the simultaneous collision detection and force estimation problem for quadrupedal locomotion using joint encoder information and the robot dynamics only. We design an interacting multiple-model Kalman filter (IMM-KF) that estimates the external force exerted on the robot and multiple possible contact modes. The method is invariant to any gait pattern design. Our approach leverages pseudo-measurement information of the external forces based on the robot dynamics and encoder information. Based on the estimated contact mode and external force, we design a reflex motion and an admittance controller for the swing leg to avoid collisions by adjusting the leg's reference motion. Additionally, we implement a force-adaptive model predictive controller to enhance balancing. Simulation ablatation studies and experiments show the efficacy of the approach.

Problem

• Simultaneous collision detection and force estimation for quadruped robots
• Designing IMM-KF for external force and contact mode estimation
• Developing reflex motion and adaptive control for collision avoidance

Innovation

• IMM-KF estimates force and contact modes
• Pseudo-measurement leverages dynamics and encoders
• Force-adaptive MPC enhances balancing control