Air Force Taps Stratom to Develop AI-Powered Asset Tracking System

The Air Force Research Laboratory has selected Stratom to develop an autonomous asset tracking system powered by machine learning and artificial intelligence.

The AATS will feature camera systems to identify and track military logistics assets across various environments and transportation modes, Stratom said.

Under the Small Business Innovation Research project, Stratom seeks to eliminate the complexities of traditional asset tracking, simplifying logistics operations and equipment coordination.

The project’s initial phase focuses on software development for machine learning models to extract data such as asset type and identification numbers from images, removing the need for physical modifications to existing assets.

Elizabeth Gilmour, Stratom’s senior robotics perception engineer, explained that the cost-effective and scalable solution uses advanced machine learning to identify and record airplane tail numbers and link them to the correct aircraft. The information will then be shared with databases and automated guided vehicles to streamline aircraft refueling and loading processes.

While initially conceived to aid autonomous refueling and cargo-loading vehicles, the AATS has already garnered interest for broader applications. Stratom said its text-spotting technology can be used for other assets such as rail cars, trailers or pallets, and in various situations beyond logistical purposes.