Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo missions) or part of a fleet (group missions), with and without constant connection with the base station. The base station acts as the command centre to manage the activities of the drones. However, an independent, localised and effective fleet control is required, potentially based on swarm intelligence, for the reasons: 1) increase in the number of drone fleets, 2) number of drones in a fleet might be multiple of tens, 3) time-criticality in making decisions by such fleets in the wild, 4) potential communication congestions lag, and 5) in some cases working in challenging terrains that hinders or mandates-limited communication with control centre (i.e., operations spanning long period of times or military usage of such fleets in enemy territory). This self-ware, mission-focused and independent fleet of drones that potential utilises swarm intelligence for a) air-traffic and or flight control management, b) obstacle avoidance, c) self-preservation while maintaining the mission criteria, d) collaboration with other fleets in the wild (autonomously) and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.
Abstract As a challenging high dimension optimization problem, three-dimensional path planning for Uninhabited Combat Air Vehicles (UCAV) mainly centralizes on optimizing the flight route with different types of constrains under complicated combating environments. An improved version of Bat Algorithm (BA) in combination with a Differential Evolution (DE), namely IBA, is proposed to optimize the UCAV three-dimensional path planning problem for the first time. In IBA, DE is required to select the most suitable individual in the bat population. By connecting the selected nodes using the proposed IBA, a safe path is successfully obtained. In addition, B-Spline curves are employed to smoothen the path obtained further and make it practically more feasible for UCAV. The performance of IBA is compared to that of the basic BA on a 3-D UCAV path planning problem. The experimental results demonstrate that IBA is a better technique for UCAV three-dimensional path planning problems compared to the basic BA model.