Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

The autonomy of unmanned aerial vehicles (UAVs) - self-governing in the aerospace discipline has been a remarkable research area with the development of the advanced bespoke microcontrollers embedded with advanced AI techniques for the last several decades. The road forward about the operational environment is certain about the swarms of fully automated UAVs (FAUAVs), that is, urban areas. FAUAVs with self-learning and self-decision-making abilities by executing non-trivial sequences of events with decimetre-level accuracy based on a set of rules, control loops and constraints using dynamic flight plans and trajectories are taking their indispensable parts within smart cities (SCs). Therefore, their integration with the SC components using real-time data analytics is urgent. This is mainly required to establish a better swarm intelligence along with a safer and optimised harmonious smart ecosystem that enables cooperative FAUAV-SC automation systems with collaborative automated intelligence engaging in the concepts of Internet of Everything (IoE) and Automation of Everything (AoE). Planning the future of cities with swarms of FAUAVs is explored in this paper to optimise the use of FAUAVs with a diverse range of applications and a contemporary methodology is proposed using a holistic framework - FAUAVinSCF equipped with various effective and efficient techniques along with a novel FAUAV routing technique customisable to the constraints of FAUAVs and urban areas. With the methodology, the components of SC and FAUAVs involving recent and impending technological advancements are moulded together to make this inevitable transformation a harmonious part of the inhabitants contributing to the cities' liveability and sustainability. The framework consists of a decentralized agent-based control architecture that monitors and controls the swarms of resource-constraint FAUAVs for their real-time requirements in optimising their urban uses. The outcomes of the methodology suggest that the constraints of FAUAVs can be mitigated significantly in urban areas and consequently, their efficacy can be increased in realising their diverse range of missions.