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General Atomics advances autonomous operational UCAV

General Atomics Aeronautical Systems, Inc (GA-ASI) advanced its ability to operationalise the unmanned combat air vehicle (UCAV) ecosystem by combining advanced autonomy and government-provided human-machine interface (HMI) hardware.

General Atomics Aeronautical Systems, Inc (GA-ASI) advanced its ability to operationalise the unmanned combat air vehicle (UCAV) ecosystem by combining advanced autonomy and government-provided human-machine interface (HMI) hardware.

A GA-ASI-owned Avenger unmanned aircraft system (UAS) was paired with “digital twin” aircraft to autonomously conduct live, virtual, and constructive (LVC) multi-objective collaborative combat missions.

The flights, which took place on 13 July 2023 from GA-ASI’s Desert Horizon Flight Operations Facility in El Mirage, California, demonstrated the company’s commitment to maturing its UCAV ecosystem for autonomous collaborative platforms.


GA-ASI senior director of advanced programs Michael Atwood said, “The concepts demonstrated by these flights set the standard for operationally relevant mission systems capabilities on UCAV platforms.”

The ecosystem’s goal is to rapidly integrate best-of-breed capabilities in areas such as artificial intelligence (AI), mission-relevant interfaces, and other capabilities from third-party providers at the speed of relevance for 21st century conflicts.

“Our integration of the emerging FoX system accelerates speed to ramp for emerging collaborative air-to-air capabilities. The combination of airborne high-performance computing, sensor fusion, human-machine teaming, and AI pilots making decisions at the speed of relevance shows how quickly GA-ASI’s capabilities are maturing as we move to operationalise autonomy for UCAVs,” Atwood added.

The team demonstrated manned-unmanned teaming (MUM-T) using the US Air Force’s Project FoX system, which included a touchscreen tablet for fighter cockpits, with the tablet providing control and monitoring of advanced autonomy while it conducted a multi-objective combat mission consisting of LVC entities.

Mission autonomy capabilities focused on optimised search and signature management. Search optimisation autonomy behaviours were provided by General Atomics’ partner Scientific Systems Company Inc (SSCI).

These skills were integrated into and orchestrated by government-furnished equipment (GFE) autonomy core architecture enhanced by GA-ASI. The flexibility of the GFE autonomy core software stack enabled rapid, seamless integration of one of SSCI’s multi-UAS behaviours.

Autonomous trajectories were calculated by SSCI algorithms and subsequently communicated to GA-ASI’s autonomy core for translation to vehicle routes. SSCI also provided an array of behaviours using its Collaborative Mission Autonomy suite where the software adapts to mission contingencies such as system failures, connectivity dropout, and combat losses to ensure successful tactical execution.

The signature management skill, based on deep reinforcement learning, was developed by GA-ASI. Skill development leveraged GA-ASI’s novel reinforcement learning (RL) architecture that was designed using agile software methodology and industry-standard tools such as Docker and Kubernetes. Commanded using the FoX tablet, the RL agent navigated to an operator-identified target while minimising the radar cross section.

This MUM-T, facilitated via open mission system (OMS) messages and alignment to the newest government architectures, demonstrated real-time operator tasking and supervision of an autonomous platform as it conducted its mission.

The team used a government-furnished autonomy core engine and the government-standard OMS messaging protocol to enable communication between the RL agents and the LVC system. Utilising government standards such as OMS will make rapid integration of autonomy for UCAVs possible. In addition, GA-ASI used a General Dynamics EMC2 to run the autonomy architecture.

EMC2 is an open architecture multi-function processor with multi-level security infrastructure to run the autonomy architecture, demonstrating the ability to bring high-performance computing resources to UCAVs to perform quickly tailorable mission sets depending on the operational environment.

GA-ASI is demonstrating its commitment to maturing an autonomy infrastructure to enable rapid integration and validation of third-party tactical software applications from an App Store and maintaining safety of flight.

This is the latest in an ongoing series of autonomous flights performed by GA-ASI using internal research and development funding to prove out important AI/ML concepts for UAS.

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