Adaptive AutoVerse Digital Twin for Accelerating Depot Operations through Virtual Testing

Autonoma’s AutoVerse platform is an advanced AI-driven digital twin simulation environment designed for high-fidelity modeling, testing, and optimization of autonomous systems, sensor-driven analytics, and logistics operations. It enables scalable, physics-accurate simulations, integrating real-time sensor fusion, AI-based decision-making, and predictive analytics to evaluate and refine complex systems before real-world deployment.The Warner Robins Air Logistics Complex (WR-ALC) faces significant challenges in validating and integrating emerging autonomous airframe inspection, foreign object debris (FOD) detection, and robotic logistics solutions. Current evaluation methods rely heavily on physical testing, which is resource-intensive, time-consuming, and disruptive to operational readiness. The absence of a comprehensive virtual testbed limits the Air Force’s ability to efficiently assess new technologies under diverse conditions.In Phase II, AutoVerse will be adapted to Air Force-specific requirements, creating a scalable, high-accuracy digital twin of WR-ALC operations to support UAV-based aircraft inspections, AI-enhanced FOD detection, and AGV-based logistics optimization. Simulating drone-based structural assessments under variable conditions will improve automation reliability, while optimizing real-time detection models for FOD will enhance accuracy and risk mitigation. Autonomous ground vehicle navigation and asset distribution within depot environments will be refined to ensure efficient operations. The platform will be engineered for DoD cybersecurity compliance, meeting NIST SP 800-171, RMF, and CMMC requirements for integration into Air Force IT infrastructure. Phase II validation will focus on operational accuracy, AI model performance, and real-world deployment feasibility, establishing a foundation for Phase III implementation and expanded DoD applications.AutoVerse has been successfully deployed in high-performance commercial environments, including its use in the Abu Dhabi Autonomous Racing League (A2RL) for 180+ mph autonomous vehicle simulation and AI-driven optimization. It has supported smart city infrastructure projects in Peachtree Corners, enabling traffic modeling, roadway design validation, and long-term infrastructure planning. Additionally, Booz Allen Hamilton has leveraged AutoVerse for large-scale reinforcement learning and imitation learning AI development in autonomous mobility applications.By leveraging high-fidelity, AI-driven digital twin technology, AutoVerse will provide WR-ALC and the broader Air Force ecosystem with a scalable, cost-effective testing environment that accelerates technology adoption, reduces operational risk, and enhances logistics and sustainment capabilities.