Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems  
[PERSON]  
1 Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA 11 Department of Computer Science, Florida Southern College, Lakeland, FL 33801, USA 11 Department of Mathematics, Florida Southern College, Lakeland, FL 33801, USA 11 Department of Engineering, The University of Tennessee at Martin, Martin, TN 38228, USA 11 Department of Electrical, Computer, Software and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA 11 Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA 11 Department of Engineering, The University of Tennessee at Martin, Martin, TN 38228, USA 11 Department of Electrical, Computer, Software and Systems Engineering, Embry-Riddle Aeronautical University  
dynamics. The centrally controlled collections of drones are popular and used for entertainment, e.g., in stadiums to emulate firework displays. However, AUAS swarm robotics have a fully distributed control architecture through independent and secure agents. The secure multi-agent approach to flight control and navigation allows the distribution of trust via consensus mechanisms and smart contracts--making them robust against cyberattacks and agent failures. Navigation and swarm control dictate multi-hop data networking between nodes of the AUAS swarm. The AUAS network requires network capacity for control functions between the agents and requires low latency for precision, navigation, and maneuvering--not to mention negotiating challenging flight paths in dense environments without collision. Thus, in this paper, we propose an integration of communication and sensor arrays to route a collection of AUAS while facilitating high-performance aerial multi-agent systems in the most challenging of air spaces. The development of 5G cellular networking enables the improvement of the capacity of beamforming on AUAS networking [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Different from the majority of the literature on UAS based on a single flying antenna system (as a hub) [14; 15; 16; 17; 18; 19], we propose an AUAS swarm routing algorithm that is compatible with wideband multi-beam beamforming and based on multiple steering angles and multiple variations to optimize the routing with a decentralized communication system to increase efficiency, accuracy, and reliability. We obtain an AUAS swarm routing algorithm in which drones can communicate with each other efficiently and understand routing through decentralized multiple antenna array systems. Frankly, the quicker and more synchronized the decisions of each drone, the easier the swarm will navigate efficiently. A model based on AUAS swarm routing becomes more reliable when the decision-making processes are transparent and explainable. In general, it is difficult to define an accurate and reliable mathematical model to optimize drone swarm routing, but it is easy to draw a definition to model swarm routing having each drone understand each other and making a decision to determine optimal routing. Deep neural network-based (DNN) machine learning (ML) algorithms provide solutions for human dynamics, channel state information (CSI), optimum detection, and array beamforming [20; 21; 22; 23; 24]. However, these are trained on simplified scenarios that either assume a single-user scenario without interface [25; 26], or require exact CSI available to the users for CSI reconstruction at the base stations [25; 27; 28], or need an end-to-end network training to design a frequency division duplex multiple input-output system [29] for swarm routing. The drone swarm can route using ML-based spatial and temporal data.