Learning to Predict Mobile Robot Stability in Off-Road Environments

2025-07-16

AI Summary

Real-time stability prediction for wheeled robots operating on unstructured off-road terrain remains challenging due to the difficulty of acquiring reliable contact forces and accurate terrain models. Method: This paper proposes a lightweight, data-driven approach that bypasses explicit physical modeling and contact-force measurements. Instead of conventional physics-based stability metrics (e.g., Static Stability Margin or Zero-Moment Point), it leverages only onboard sensory signals—namely IMU readings and wheel speed—to train a compact neural network (IMUnet) for end-to-end stability estimation. Crucially, we introduce the C3 score—a measurable stability proxy derived from ArUco marker-based visual tracking—as a calibration-free, physics-agnostic supervisory signal. Results: Experimental validation across diverse terrains and velocities demonstrates strong generalization capability and real-time performance (<10 ms inference latency). The method achieves sufficient prediction accuracy to support stability-aware motion planning in demanding applications such as agricultural inspection and planetary surface exploration.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Predict mobile robot stability on rugged terrain
• Estimate stability without force or terrain data
• Develop vision-based stability score for training

Innovation

• Lightweight neural network for stability estimation
• Vision-based ArUco tracking for stability scoring
• IMU and velocity inputs for unseen conditions