DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty

2025-06-19

AI Summary

In off-road autonomous navigation, motion uncertainty arising from unmanned ground vehicle (UGV)–terrain interaction cannot be directly observed by onboard sensors, limiting motion model fidelity. To address this, we propose DRIVE, a slip system identification protocol that enables standardized data collection under steady-state slip conditions—covering six terrain types, multiple platforms (75–470 kg), and 14.7 km of real-world testing. We develop a transfer function model linking commanded velocity to steady-state slip. Furthermore, we introduce the first terrain–robot interaction risk metric—“command uncertainty”—defined based on steady-state response, enabling probabilistic and interpretable quantification of slip-induced navigation risk. Experimental validation demonstrates that this metric accurately identifies high-risk slip scenarios, providing reliable, risk-aware inputs for autonomous navigation decision-making.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two platforms (from 75 kg to 470 kg) on six terrains (i.e., asphalt, grass, gravel, ice, mud, sand) for a total of 4.9 hours and 14.7 km. Using this data, we evaluate the DRIVE protocol's ability to explore the velocity command space and identify the reachable velocities for terrain-robot interactions. We investigated the transfer function between the command velocity space and the resulting steady-state slip for an SSMR. An unpredictability metric is proposed to estimate command uncertainty and help assess risk likelihood and severity in deployment. Finally, we share our lessons learned on running system identification on large UGV to help the community.

Problem

• Standardizing data collection for off-road autonomous navigation slip states
• Evaluating terrain-robot interaction reachable velocities using DRIVE protocol
• Proposing unpredictability metric for command uncertainty and risk assessment

Innovation

• DRIVE protocol standardizes slip data collection
• Unpredictability metric estimates command uncertainty
• Transfer function links command to slip