General Atomics Aeronautical Systems (GA-ASI) further advanced its Collaborative Combat Aircraft (CCA) ecosystem by flying three unique missions with artificially intelligent (AI) pilots on an operationally relevant Open Mission System (OMS) software stack, the company writes in a press release. A company-owned Avenger 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 in December 2022, were to demonstrate the company’s commitment to maturing its CCA ecosystem for Autonomous Collaborative Platform (ACP) UAS using Artificial Intelligence (AI) and Machine Learning (ML). According to the company, this is to provide a new and innovative tool for next-generation military platforms to make decisions under dynamic and uncertain real-world conditions.
The flight used GA-ASI’s Reinforcement Learning (RL) architecture built using agile software development methodology and industry-standard tools such as Docker and Kubernetes to develop and validate three deep learning RL algorithms in an operationally relevant environment. RL agents demonstrated single, multi, and hierarchical agent behaviours. The single agent RL model navigated the live plane while avoiding threats to accomplish its mission. Multi-agent RL models flew a live and virtual Avenger to collaboratively chase a target while avoiding threats. The hierarchical RL agent used sensor information to select courses of action based on its understanding of the world state.
For the missions, real-time updates were made to flight paths based on fused sensor tracks provided by virtual Advanced Framework for Simulation, Integration, and Modeling (AFSIM) models, and RL agent missions were selected by operators while the plane was airborne. This live operational data describing AI pilot performance is planned to be fed into GA-ASI’s rapid retaining process for analysis and used to refine future agent performance.