Uncovering drone intentions using control physics informed machine learning

Perrusquía, Adolfo; Guo, Weisi; Fraser, Benjamin; Wei, Zhuangkun

Abstract

Unmanned 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.

Citation

Perrusquía, A., Guo, W., Fraser, B., & Wei, Z. (2024). Uncovering drone intentions using control physics informed machine learning. Communications Engineering, 3(1), Article 36. https://doi.org/10.1038/s44172-024-00179-3

• Journal Article Type: Article
• Acceptance Date: Feb 7, 2024
• Online Publication Date: Feb 24, 2024
• Publication Date: Feb 24, 2024
• Deposit Date: Feb 12, 2025
• Journal: Communications Engineering (Nature Publication Group)
• Print ISSN: 2731-3395
• Electronic ISSN: 2731-3395
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 1
• Article Number: 36
• DOI: https://doi.org/10.1038/s44172-024-00179-3
• Public URL: https://durham-repository.worktribe.com/output/3479298