Comparing Motion Distortion Between Vehicle Field Deployments

TL;DR

This paper develops a classification system and a motion distortion metric to evaluate UGV deployment challenges in rough terrains, enhancing understanding and validation of autonomous navigation performance.

Contribution

It introduces a new classification system and a motion distortion metric that do not require explicit feature quantification, aiding in assessing UGV navigation in complex environments.

Findings

Mapping UGV deployments relative to kinetic energy and terrain complexity.

The motion distortion metric effectively assesses navigation performance.

Case study demonstrates impact of motion distortion features on modeling accuracy.

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.