From Impact to Insight: Dynamics-Aware Proprioceptive Terrain Sensing on Granular Media

2026-04-02

AI Summary

Traditional quasi-static assumptions fail to accurately estimate granular terrain properties under high-speed motion, leading to distorted contact force perception. This work addresses this limitation by conducting high-dynamic jumping experiments, which reveal—for the first time—that an acceleration-dependent added-mass effect dominates the transient force response in granular media. To account for this phenomenon, the study proposes an acceleration-aware terrain parameter estimation framework that integrates proprioception, a momentum observer, and weighted regression to construct a physics-informed force decomposition model explicitly capturing the added-mass effect. By transcending quasi-static constraints, the method robustly recovers granular terrain stiffness parameters across diverse high-velocity conditions, with estimates showing strong agreement with ground-truth measurements obtained from a linear actuator.

Abstract

Robots that traverse natural terrain must interpret contact forces generated under highly dynamic conditions. However, most terrain characterization approaches rely on quasi-static assumptions that neglect velocity- and acceleration-dependent effects arising during impact and rapid stance transitions. In this work, we investigate granular terrain interaction during high-speed hopping and develop a physics-based framework for dynamic terrain characterization using proprioceptive sensing alone. Through controlled hopping experiments with systematically varied impact speed and leg compliance, our measurements reveal that quasi-static based assumptions lead to large discrepancies in granular terrain property estimation during high-speed hopping, particularly upon touchdown and controller-induced stiffness transitions. Velocity-dependent drag alone cannot explain these discrepancies. Instead, acceleration-dependent added-mass effects-associated with grain entrainment beneath the foot-dominate transient force responses. We integrate this force decomposition with a momentum-observer-based estimator that compensates for rigid-body inertia and gravity, and introduce an acceleration-aware weighted regression to account for increased force variance during high-acceleration events. Together, these methods enable consistent recovery of granular stiffness parameters across locomotion conditions, closely matching linear-actuator ground truth. Our results demonstrate that accurate terrain inference during high-speed locomotion requires explicit treatment of acceleration-dependent granular effects, and provide a foundation for robots to characterize complex deformable terrain during dynamic exploration of terrestrial and planetary environments.

Problem

• dynamic terrain sensing
• granular media
• proprioception
• high-speed locomotion
• acceleration-dependent effects

Innovation

• dynamic terrain sensing
• proprioceptive perception
• granular media
• added-mass effect
• acceleration-aware regression