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Safety and efficiency are primary goals of air traffic management. With the integration of unmanned aerial vehicles (UAVs) into the airspace, UAV traffic management (UTM) has attracted significant interest in the research community to maintain the capacity of three-dimensional (3D) airspace, provide information, and avoid collisions. We propose a new decision-making architecture for UAVs to avoid collision by formulating the problem into a multi-agent game in a 3D airspace. In the proposed game-theoretic approach, the Ego UAV plays a repeated two-player normal-form game, and the payoff functions are designed to capture both the safety and efficiency of feasible actions. An optimal decision in the form of Nash equilibrium (NE) is obtained. Simulation studies are conducted to demonstrate the performance of the proposed game-theoretic collision avoidance approach in several representative multi-UAV scenarios. 

Random mobility models (RMMs) capture the statistical movement characteristics of mobile agents and play an important role in the evaluation and design of mobile wireless networks. Particularly, RMMs are used to model the movement of unmanned aerial vehicles (UAVs) as the platforms for airborne communication networks. In many RMMs, the movement characteristics are captured as stochastic processes constructed using two types of independent random variables. The first type describes the movement characteristics for each maneuver and the second type describes how often the maneuvers are switched. We develop a generic method to estimate RMMs that are composed of these two types of random variables. Specifically, we formulate the dynamics of movement characteristics generated by the two types of random variables as a special Jump Markov System and develop an estimation method based on the Expectation–Maximization principle. Both off-line and on-line variants of the method are developed. We apply the estimation method to the Smooth–Turn RMM developed for fixed-wing UAVs. The simulation study validates the performance of the proposed estimation method. We further conduct a UAV experimental study and apply the estimation methods to real UAV trajectories. 

In recent years, networked airborne computing (NAC) has emerged as a promising paradigm because it can leverage the collaborative capabilities of unmanned aerial vehicles (UAVs) for distributed computing tasks. Despite the burgeoning interests in NAC and UAV-based computing, many existing studies depend on over-simplified simulations for performance evaluation. This reliance has led to a gap in our understanding of NAC’s true potential and challenges. To fill this gap, this paper presents a comprehensive approach: the creation of a realistic simulator and a novel hardware testbed. The simulator, developed using ROS and Gazebo, emulates networked UAVs, focusing on resource-sharing and distributed computing capabilities. This tool offers a cost-effective, scalable, and adaptable environment, making it ideal for preliminary investigations across a myriad of real-world scenarios. In parallel, our hardware testbed comprises multiple quadrotors, each equipped with a Pixhawk control unit, a Raspberry Pi computing module, a real-time kinematic (RTK) positioning system, and multiple communication units. Through extensive simulations and hardware tests, we delve into the key determinants of NAC performance, such as computation task size, number of UAVs, communication quality, and UAV mobility. Our findings not only underscore the inherent challenges in optimizing NAC performance but also provide pivotal insights for future enhancements. These insights encompass refining the simulator, reducing computation overheads, and equipping the hardware testbed with cutting-edge communication devices.