Automated On-Entry Decision-Making for UTM Zones Based on Reputations and Certifications

This work presents our progress for automating on-entry decisions for small uncrewed aerial systems (sUAS) that request access to UTM-managed flight zones. These decisions consider current environmental conditions, and sUAS and operator reputations and credentials. Reputations are established dynamically over time from flight histories of successful or problematic flights within UTM zones, while credentials certify capabilities for operating in current environmental conditions including airworthiness of the drone, sensing and maneuvering capabilities, and alignment with the current environmental conditions and flight plans. In this paper, we describe these factors, present a cloud-based architecture for supporting this approach across diverse sUAS, and report on preliminary proof-of-concept experiments conducted in simulation.

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Published
2025-07-16
DOI
https://doi.org/10.2514/6.2025-3567
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

Automated On-Entry Decision-Making for UTM Zones Based on Reputations and Certifications

Theodore Chambers, Md Nafee Al Islam, Robyn R. Lutz, Myra B. Cohen et al.
Advanced Manufacturing and Logistics Optimization
article

Automated On-Entry Decision-Making for UTM Zones Based on Reputations and Certifications

Theodore Chambers, Md Nafee Al Islam, Robyn R. Lutz, Myra B. Cohen, Jane Cleland‐Huang, Wenyi Tang, Arturo Miguel Russell Bernal, Usman Gohar, Taeho Jung, Michael C. Hunter, Pedro Antonio Alarcón Granadeno
article en

Abstract

This work presents our progress for automating on-entry decisions for small uncrewed aerial systems (sUAS) that request access to UTM-managed flight zones. These decisions consider current environmental conditions, and sUAS and operator reputations and credentials. Reputations are established dynamically over time from flight histories of successful or problematic flights within UTM zones, while credentials certify capabilities for operating in current environmental conditions including airworthiness of the drone, sensing and maneuvering capabilities, and alignment with the current environmental conditions and flight plans. In this paper, we describe these factors, present a cloud-based architecture for supporting this approach across diverse sUAS, and report on preliminary proof-of-concept experiments conducted in simulation.

University of Notre Dame (US), University of San Diego (US), Iowa State University (US), Notre Dame College (US), Notre Dame University (PH)
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Advanced Manufacturing and Logistics Optimization
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Automated On-Entry Decision-Making for UTM Zones Based on Reputations and Certifications — Theodore Chambers, Md Nafee Al Islam, et al. · (2025) | TGRS Research Map | TGRS