Comparing Motion Distortion Between Vehicle Field Deployments

TL;DR

The paper addresses the challenge of comparing UGV motion across diverse terrains by proposing a terrain-agnostic motion distortion metric that captures the difference between ideal slip-free motion and actual vehicle velocity. It categorizes motion distortion features into internal and external and demonstrates how deployments can be mapped by vehicle kinetic energy and terrain complexity. The main contribution is a simple, data-efficient metric that enables cross-dataset comparisons and a case study across four DRIVE protocol datasets, illustrating how terrain and vehicle dynamics influence motion distortion. The work emphasizes the need for a shared, extensible motion-distortion database and identifies limitations related to transitory motion, proposing directions for future enhancement and validation frameworks with broader data integration.

Abstract

Recent advances in autonomous driving for uncrewed ground vehicles (UGVs) have spurred significant development, particularly in challenging terrains. This paper introduces a classification system assessing various UGV deployments reported in the literature. Our approach considers motion distortion features that include internal UGV features, such as mass and speed, and external features, such as terrain complexity, which all influence the efficiency of models and navigation systems. We present results that map UGV deployments relative to vehicle kinetic energy and terrain complexity, providing insights into the level of complexity and risk associated with different operational environments. Additionally, we propose a motion distortion metric to assess UGV navigation performance that does not require an explicit quantification of motion distortion features. Using this metric, we conduct a case study to illustrate the impact of motion distortion features on modeling accuracy. This research advocates for creating a comprehensive database containing many different motion distortion features, which would contribute to advancing the understanding of autonomous driving capabilities in rough conditions and provide a validation framework for future developments in UGV navigation systems.

Figures (4)

• Figure 1: Examples of motion distortion features that affect UGV motion. Heavier vehicles with higher top speeds have more inertia (A, B). Aggressive motion leads to transitory behavior and non-linear dynamics as opposed to slower, conservative driving (A, B). Terrain steepness and roughness also lead to slippage and loss of contact between wheels and terrain (C, D). Lastly, terrain hardness and friction significantly impact vehicle motion (E, F). In F, the Warthog is navigating on a resurfaced ice rink.
• Figure 2: Qualitative mapping of the related work based on the motion distortion features respectively represented by the maximum kinetic energy of the vehicles used in the experiment and the most complex terrain used in the experiment. The type of motion model developed by the authors is also presented using different markers. The area created by the internal and external motion distortion features is also divided based on the risk associated with mobile robot deployment in these conditions. One should note the logarithm scale used for the y-axis.
• Figure 3: A 2D planar representation of our model formulation's important variables. The ideal motion $^{\mathcal{R}}\bm{f}$ is represented in orange. The estimated resulting body velocity $^{\mathcal{R}}\bm{v}$ is represented in green. Motion distortion $^{\mathcal{R}}\bm{g}$, or body-level slip, is shown in purple. Angular components for all velocities are represented as $(\cdot)_\omega$. Linear components for all velocities are represented as $(\cdot)_l$ For the case of SSMR, ideal motion is parameterized by wheel radius $r$ and vehicle width $b$.
• Figure 4: Motion distortion moduli were calculated on four datasets collected with the DRIVE protocol. A Clearpath Husky A200 was used to record data separately on tile and on snow. The two other datasets were collected with a Warthog on gravel and on ice separately. The Warthog weighs 470kg and has a maximum speed of 5m/s while the Husky has a maximum speed of 1m/s and weighs 75kg.