Learning to Predict Mobile Robot Stability in Off-Road Environments

TL;DR

This paper introduces a learning-based method using neural networks and vision-based scoring to estimate mobile robot stability in off-road environments, bypassing the need for detailed terrain models and force sensors.

Contribution

It presents a novel approach combining IMU data and a vision-based stability score for real-time stability estimation without explicit terrain modeling.

Findings

The neural network accurately predicts stability across various terrains.

The vision-based C3 score effectively correlates with physical instability.

Method generalizes well to unseen conditions.

Abstract

Navigating in off-road environments for wheeled mobile robots is challenging due to dynamic and rugged terrain. Traditional physics-based stability metrics, such as Static Stability Margin (SSM) or Zero Moment Point (ZMP) require knowledge of contact forces, terrain geometry, and the robot's precise center-of-mass that are difficult to measure accurately in real-world field conditions. In this work, we propose a learning-based approach to estimate robot platform stability directly from proprioceptive data using a lightweight neural network, IMUnet. Our method enables data-driven inference of robot stability without requiring an explicit terrain model or force sensing. We also develop a novel vision-based ArUco tracking method to compute a scalar score to quantify robot platform stability called C3 score. The score captures image-space perturbations over time as a proxy for physical instability and is used as a training signal for the neural network based model. As a pilot study, we evaluate our approach on data collected across multiple terrain types and speeds and demonstrate generalization to previously unseen conditions. These initial results highlight the potential of using IMU and robot velocity as inputs to estimate platform stability. The proposed method finds application in gating robot tasks such as precision actuation and sensing, especially for mobile manipulation tasks in agricultural and space applications. Our learning method also provides a supervision mechanism for perception based traversability estimation and planning.