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1. Anisotropic flocking control of distributed multi-agent systems using fluid abstraction

   Silic, M.; Song, Z.; Mohseni, K. (January 2018, Proceedings of the AIAA Information Systems-AIAA Infotech @ Aerospace)

   This paper presents a multi-agent flocking scheme for real-time control of homogeneous unmanned aerial vehicles (UAVs) based on smoothed particle hydrodynamics. Swarm cohe- sion, collision avoidance, and velocity consensus are concurrently satisfied by characterizing the emerging macroscopic flock as a continuous fluid. Two vital implementation issues are addressed in particular including latency in information fusion and directionality of com- munication due to antenna patterns. Symmetric control forces are achieved by meticulous scheduling of inter-vehicle communication to sustain the motion stability of the flock. A gener- alized, anisotropic smoothing kernel that takes into account the relative position and attitude between agents is adopted to address potential flocking instability introduced by communi- cation anisotropy due to the antenna radiation pattern. The feasibility of the technique is demonstrated experimentally using a single UAV avoiding a virtual obstacle. 

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2. Development of a Laboratory Platform for UAV Cybersecurity Education

   Jiang, Yushan; Yuan, Jiawei; Sun, Lulu; Song, Houbing (January 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   There is an increasing need to fly unmanned aerial vehicles (UAVs) to enable a wide variety of beneficial applications such as emergency/disaster response, observation and study of weather phenomena including severe storms. However, UAVs are subject to cybersecurity threats stemming from increasing reliance on computer and communication technologies. There is a need to foster a robust workforce with integrated UAV and cybersecurity competencies. In addition to technique challenges, current UAV cybersecurity education also faces two significant non-technical challenges: first, there are federal or state rules and regulations on UAV flights; second, the number of designated UAV test sites is limited. A three years NSF SaTC funded project in 2020 will specifically address these challenges. We propose to develop a laboratory platform for UAV cybersecurity education. To be specific, our platform integrates software simulation with hardware-in-the-loop (HIL) simulation to simulate different UAV scenarios, on the top of which cybersecurity components are developed for hands-on practicing. We use a firmware for UAV system development, Pixhawk with related open-source software packages, as the basic simulation framework. On the top of the simulation environment, a series of hands-on exercise modules will be developed to cover UAV cybersecurity issues. Motivated by different types of cybersecurity threats to UAVs, we will adopt the scenario based design and set up several categories of exercise modules including common threats in UAV and additional modules for newly identified threats with corresponding actors, goals, actions, and events. In such a manner offense and defense tasks can be further developed. The proposed platform has the potential to be adopted by universities with limited resources to UAV cybersecurity. It will help educate future workforce with integrated UAV and cybersecurity competencies, towards secure and trustworthy cyberspace around UAVs. 

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3. 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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4. Collaborative multi-robot multi-human teams in search and rescue.

   Williams, R. (April 2020, Proceedings of the International ISCRAM Conference)

   Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 

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5. Outage-Optimized Deployment of UAVs

   Shabanighazikelayeh, Maryam; Koyuncu, Erdem (September 2019, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications)

   We consider multiple unmanned aerial vehi- cles (UAVs) serving a density of ground terminals (GTs) as mobile base stations. The objective is to minimize the outage probability of GT-to-UAV transmissions. In this context, the optimal placement of UAVs under different UAV altitude constraints and GT densities is studied. First, using a random deployment argument, a general upper bound on the optimal outage probability is found for any density of GTs and any number of UAVs. Lower bounds on the performance of optimal deployments are also deter- mined. The upper and lower bounds are combined to show that the optimal outage probability decays exponentially with the number of UAVs for GT densities with finite support. Next, the structure of optimal deployments are studied when the common altitude constraint is large. In this case, for a wide class of GT densities, it is shown that all UAVs should be placed to the same location in an optimal deployment. A design implication is that one can use a single multi-antenna UAV as opposed to multiple single-antenna UAVs without loss of optimality. Numerical optimization of UAV deployments are carried out using particle swarm optimization. Simulation results are also presented to confirm the analytical findings. 

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