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1. A Safety Constrained Control Framework for UAVs in GPS Denied Environment

   https://doi.org/10.1109/CDC42340.2020.9304304  

   Wan, Wenbin ; Kim, Hunmin ; Hovakimyan, Naira ; Sha, Lui ; Voulgaris, Petros G. ( December 2020 , 59th IEEE Conference on Decision and Control)

   null (Ed.)

   Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can lead to potentially dangerous situations. To avoid intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a safety constrained control framework that adapts the UAV at a path re-planning level to support resilient state estimation against GPS spoofing attacks. The attack detector is used to detect GPS spoofing attacks and provides a switching criterion between the robust control mode and emergency control mode. An attacker location tracker (ALT) is developed to track the attacker's location and estimate the spoofing device's output power by the unscented Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT, we design an escape controller (ESC) based on the model predictive controller (MPC) such that the UAV escapes from the effective range of the spoofing device within the escape time. 

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2. Counter UAV Swarms: Challenges, Considerations, and Future Directions in UAV Warfare

   https://doi.org/10.1109/MWC.003.2400047  

   Sherman, Michelle ; Shao, Sihua ; Sun, Xiang ; Zheng, Jun ( January 2024 , IEEE Wireless Communications)

   Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results. 

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3. Explainable AI for Prioritizing and Deploying Defenses for Cyber-Physical System Resiliency

   https://doi.org/10.1109/TPS-ISA58951.2023.00032  

   Ray, Indrajit ; Sreedharan, Sarath ; Podder, Rakesh ; Bashir, Shadaab Kawnain ; Ray, Indrakshi ( November 2023 , IEEE)

   The adoption of digital technology in industrial control systems (ICS) enables improved control over operation, ease of system diagnostics and reduction in cost of maintenance of cyber physical systems (CPS). However, digital systems expose CPS to cyber-attacks. The problem is grave since these cyber-attacks can lead to cascading failures affecting safety in CPS. Unfortunately, the relationship between safety events and cyber-attacks in ICS is ill-understood and how cyber-attacks can lead to cascading failures affecting safety. Consequently, CPS operators are ill-prepared to handle cyber-attacks on their systems. In this work, we envision adopting Explainable AI to assist CPS oper-ators in analyzing how a cyber-attack can trigger safety events in CPS and then interactively determining potential approaches to mitigate those threats. We outline the design of a formal framework, which is based on the notion of transition systems, and the associated toolsets for this purpose. The transition system is represented as an AI Planning problem and adopts the causal formalism of human reasoning to asssit CPS operators in their analyses. We discuss some of the research challenges that need to be addressed to bring this vision to fruition. 

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4. Biased Electropermanent Magnetic Docking Design for Neutral Buoyancy UAV Deployment

   https://doi.org/10.2514/6.2024-1694  

   Martin, Jacob ; Satme, Joud ; Downey, Austin R. ( January 2024 , AIAA SCITECH 2024 Forum.)

   This paper introduces a novel approach to enhance the docking mechanism of sensor packages deployed on bridges using unmanned aerial vehicles (UAVs). The current electropermanent magnet (EPM) system faces challenges in achieving efficient and stable docking due to factors such as airflow, GPS stabilization, and the time required for EPM activation. To address these issues, a biased EPM design is proposed, utilizing additional permanent magnets to achieve neutral buoyancy during UAV deployment. This design optimally balances the weight of the drone and sensor package, providing advantages such as improved stability against external factors and reduced pilot fatigue. Experimental results demonstrate the feasibility of the proposed design, indicating enhanced hold force and an extended range for efficient docking. 

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5. Monitoring CPS at Runtime - A Case Study in the UAV Domain

   https://doi.org/10.1109/SEAA.2018.00022  

   Vierhauser, Michael ; Cleland-Huang, Jane ; Bayley, Sean ; Krismayer, Thomas ; Rabiser, Rick ; Grunbacher, Paul ( August 2018 , Euromicro Conference on Software Engineering and Advanced Applications)

   Unmanned aerial vehicles (UAVs) are becoming increasingly pervasive in everyday life, supporting diverse use cases such as aerial photography, delivery of goods, or disaster reconnaissance and management. UAVs are cyber-physical systems (CPS): they integrate computation (embedded software and control systems) with physical components (the UAVs flying in the physical world). UAVs in particular and CPS in general require monitoring capabilities to detect and possibly mitigate erroneous and safety-critical behavior at runtime. Existing monitoring approaches mostly do not adequately address UAV CPS characteristics such as the high number of dynamically instantiated components, the tight integration of elements, and the massive amounts of data that need to be processed. In this paper we report results of a case study on monitoring in UAVs. We discuss CPS-specific monitoring challenges and present a prototype we implemented by extending REMINDS, a framework for software monitoring so far mainly used in the domain of metallurgical plants. Additionally, we demonstrate the applicability and scalability of our approach by monitoring a real control and management system for UAVs in simulations with up to 30 drones flying in an urban area. 

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