US Air Force deploys autonomous drones to inspect fuel lines and prevent logistics failures.

According to information published by U.S. Air Force on May 27, 2025, the U.S. Air Force 378th Expeditionary Logistics Readiness Squadron (ELRS) has begun implementing a new drone-based method for inspecting fuel infrastructure within the U.S. Central Command area of responsibility. The initiative involves the operational use of the Parrot ANAFI USA drone to conduct aerial inspections of fuel lines and bladders across the installation. Developed in coordination with the U.S. Air Forces Central Command Battle Lab, this process is designed to enhance inspection procedures by reducing human exposure to potentially hazardous environments while ensuring the continuity of essential fuel delivery operations at deployed airbases.
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The Parrot ANAFI USA drone features a 32x zoom camera for high-resolution visual assessments and thermal imaging to detect irregular heat signatures that may indicate leaks or structural compromise. (Picture source: US DoD)

The 378th ELRS is a component of the 378th Air Expeditionary Wing, which supports U.S. and partner forces from Prince Sultan Air Base in Saudi Arabia. The wing is tasked with delivering logistics and combat support across the CENTCOM theater. Within this framework, the ELRS operates the largest tactical fuel site in the AFCENT area. This infrastructure includes a network of bladders and pipelines that are critical to sustaining combat airpower. Conventional inspection procedures typically require personnel to walk along extended segments of fuel lines to check for leaks or damage, often under harsh environmental conditions. These activities are time-consuming and expose operators to elevated safety risks, prompting a shift toward remote inspection technologies.

The Parrot ANAFI USA drone employed by the 378th ELRS is equipped with dual electro-optical and thermal sensor systems. It features a 32x zoom camera for high-resolution visual assessments and thermal imaging to detect irregular heat signatures that may indicate leaks or structural compromise. Weighing approximately 500 grams, the drone has a foldable airframe that enables compact storage and transport. It offers a maximum flight endurance of 32 minutes and can cover extensive operational areas in a single sortie. Its deployment reduces the need for ground crews to physically access each segment of the fuel distribution network, thereby limiting risk exposure and streamlining logistics operations.

The system also incorporates artificial intelligence and machine learning algorithms, enabling the drone to identify visual and thermal anomalies in real time. Data gathered during flight is processed to detect signs of infrastructure degradation, with alerts transmitted to maintenance teams for further investigation. To support the effective deployment of this technology, the AFCENT Battle Lab collaborated with George Mason University and the Office of Naval Research. These organizations contributed to the software certification process and helped design training programs for drone operators. Certification ensures that the systems perform as intended under operational conditions, while structured training enhances operator proficiency in both data collection and analysis.

This inspection approach introduces a standardized, systematic, and repeatable method for monitoring fuel infrastructure. It enables more frequent inspections and faster identification of anomalies, resulting in reduced inspection cycle times and improved accuracy. The method allows fuel system personnel to survey multiple bladders and extensive line segments with minimal manpower and reduced exposure. This is aligned with operational requirements to maintain fuel availability under continuous mission tempo, supporting sustained air operations without additional resource allocation or equipment downtime. A drop in fuel availability during such conditions could delay mission execution, reduce the range and endurance of combat and support vehicles, and limit the ability to reposition forces, thereby increasing their vulnerability to enemy actions.

The drone-based inspection process developed by the 378th ELRS may be evaluated for broader implementation across other logistics and maintenance units operating under similar conditions. The model demonstrates how commercially available systems, once properly evaluated and adapted, can improve specific operational functions in the field. As the AFCENT Battle Lab continues to explore technological integration, its partnerships with research institutions remain central to validating emerging capabilities. The implementation of this system reflects a wider effort to meet operational demands through autonomous or semi-autonomous platforms suited for low-risk, repetitive tasks in infrastructure maintenance, especially to ensure effective fuel management. Sustained operations with inadequate fuel supplies exert significant pressure on logistics units to prioritize and ration limited resources, potentially forcing difficult decisions about fuel allocation that may affect overall mission success.