Test Methodologies for Collision Tolerance, Navigation, and Trajectory-Following Capabilities of Small Unmanned Aerial Systems

SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and trajectory following for commercial sUAS platforms in GPS-denied indoor environments. We also propose numerical and categorical metrics—based on established vehicle collision protocols such as the Modified Acceleration Severity Index (MASI) and Maximum Delta V (MDV)—to quantify collision resilience; for example, the tested platforms achieved an average MASI of 0.1 g, while demonstrating clear separation between the highest- and lowest-performing systems. The experimental results revealed that performance varied significantly with mission complexity, obstacle proximity, and trajectory requirements, identifying platforms best suited for subterranean or crowded indoor applications. By aggregating these metrics, users can select the optimal drone for their specific mission requirements in challenging enclosed spaces.

Authors

Institutions

Publication Details

Journal
Drones
Published
2025-06-18
DOI
https://doi.org/10.3390/drones9060447
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Test Methodologies for Collision Tolerance, Navigation, and Trajectory-Following Capabilities of Small Unmanned Aerial Systems

Edwin Meriaux, Kshitij Jerath
Drones
Robotic Path Planning Algorithms
article

Test Methodologies for Collision Tolerance, Navigation, and Trajectory-Following Capabilities of Small Unmanned Aerial Systems

Edwin Meriaux, Kshitij Jerath
article en

Abstract

SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and trajectory following for commercial sUAS platforms in GPS-denied indoor environments. We also propose numerical and categorical metrics—based on established vehicle collision protocols such as the Modified Acceleration Severity Index (MASI) and Maximum Delta V (MDV)—to quantify collision resilience; for example, the tested platforms achieved an average MASI of 0.1 g, while demonstrating clear separation between the highest- and lowest-performing systems. The experimental results revealed that performance varied significantly with mission complexity, obstacle proximity, and trajectory requirements, identifying platforms best suited for subterranean or crowded indoor applications. By aggregating these metrics, users can select the optimal drone for their specific mission requirements in challenging enclosed spaces.

DronesVol. 9(6)
University of Massachusetts Lowell (US)
U.S. Army Combat Capabilities Development Command Soldier Center
Openalex Percentile: Top 88%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Test Methodologies for Collision Tolerance, Navigation, and Trajectory-Following Capabilities of Small Unmanned Aerial Systems — Edwin Meriaux, Kshitij Jerath · Drones (2025) | TGRS Research Map | TGRS