Perrusquía, AdolfoGuo, WeisiFraser, BenjaminWei, Zhuangkun2024-03-182024-03-182024-02-24Perrusquía A, Guo W, Fraser B, Wei Z. (2024) Uncovering drone intentions using control physics informed machine learning. Communications Engineering, Volume 3, February 2024, Article number 362731-3395https://doi.org/10.1038/s44172-024-00179-3https://dspace.lib.cranfield.ac.uk/handle/1826/21018Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. 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 using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.en-UKAttribution 4.0 InternationalArticle