Uncovering Drone Intentions using Control Physics Informed Machine Learning

Abstract Fully autonomous aerial platforms or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity nor file flight plans and can pose a potential risk to a variety of critical infrastructures. Understanding an uncooperative drone's intention is important to assigning risk and executing countermeasures. Drones have rapidly changing design, flexible capabilities, and diverse underpinning algorithms. This makes distinguishing malicious from naive intentions across platforms difficult. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes (e.g., via inverse learning) using observational data alone is inherently unreliable due to observational and learning bias. Here we develop a novel control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes, out performing over expert-defined anomaly detection and inverse learning approaches. Our CPhy-ML complementary learning couples the representation power of deep learning with the conservation laws of aerospace control models, reducing bias and instability. The results in simulation and experimentation demonstrate that we can find common intention patterns across the inferred intention classes, ranging from trajectory to reward goals. We believe that this framework can provide deeper insight into the complex nature of intention and a firm step towards to its smooth integration in current counter drone technologies. The techniques developed here can aid the enforcement of drone incursions in a time where geo-fencing is no-longer efficiently enforceable. As transport and operational autonomy is increasingly used, enforcement algorithms can also inform the design of better criminal justice system and safer autonomous systems themselves.