Drones, also known as unmanned aerial vehicles (UAVs), have gained numerous applications due to their low cost, ease of use, vertical takeover and landing, and ability to operate in high-risk or hard-to-reach areas. The contribution of this review is that of building the bridge between drone technology and its application and advancements in the field of Geographic Information System (GIS). The integration of drones and GIS is valuable as it reduces costs and improves accessibility for geospatial data collection. Traditional methods involving aircraft for aerial photography are expensive, requiring the hiring of aircraft, pilots, and photographers. Drones equipped with advanced cameras and artificial intelligence software can replace the conventional technique and at the same time, be economical and time-efficient. The integration of drones and GIS is expected to bring revolutionary benefits in the fields of precision agriculture, urban planning, emergency health response, disaster management, the development of smart cities, food delivery, etc. In this paper, a state-of-the-art review of the deployment of drone-integrated GIS applications in different fields is presented. Numerous techniques and associated challenges related to their development, formulation, implementation, and regulation are highlighted. It has been concluded that drone-integration solutions in GIS improve efficiency and accuracy, enhance the decision-making process, and facilitate better real-time monitoring. The findings of this review paper are intended to help and benefit researchers, business developers, emergency service providers, industrialists, and policymakers.

In this paper a bias eliminated system identification method for an output error model is proposed. The system under consideration is a time delayed system. The identification is done via a discrete time (DT) model with non-integer time delay. In a Discrete time, system, the time delay is a non-integer multiple of the sampling period. The paper proposes a new formulation of discretization, a Single-input and a single-output system (SISO) is modified to two-inputs and single-output system (TISO): First transfer function (T. F) represents the system dynamics and the other is due to the influence of the time delay. Using this interpretation, we proceed with the estimation of the parameters. we propose the identification using three different techniques, Least Square, Regularized Least Square and instrumental variable method and verify its effectiveness through numerical examples.

In this paper, a biologically inspired cooperative prey hunting strategy is implemented for a swarm of UAVs. The cooperative hunting strategy is based on diffusion and adaptation algorithms. Diffusion and adaptation algorithms exhibit features of self-organization to create a mobile adaptive networks. Nodes in mobile adaptive networks are equipped with both learning and mobility capabilities, as they interact with one another on a local level to find solutions to distributed processing and distributed inference issues. The findings contribute to an understanding of the dynamic network structures that emerge during interactions between swarm of fish and the predators. The Swarm of fish and the predators are modelled using the unicycle model moving in a 2D plane. The swarm of fish forage in search of food which are attacked by the predators which follow hunting strategy defined by the state transition model. An optimal PSO tuned Fractional Order PID controller is designed to ensure the trajectory tracking of the UAVs. Simulation results exhibit the effectiveness of the work.

This paper presents the implementation of a cooperative prey hunting strategy for a swarm of unmanned aerial vehicles (UAVs) inspired by the collective behavior of fish and predators. The swarm of UAVs are modelled using diffusion and adaptation algorithms, which enable self-organization and create mobile adaptive networks. Nodes in the mobile adaptive networks interact with each other locally to solve issues of distributed processing and inference. The predator motion is modelled using a state transition model. The study provides insights into the dynamic network structures that arise during interactions between a swarm of fish and predators. The dynamic model of the swarm of fish and predators are modeled using the unicycle model on a coplanar surface. A Lyapunov-based Backstepping controller is designed to ensure that the UAVs track their trajectories accurately. From the simulation results, it can be observed that both the swarm UAVs and the predator successfully tracks the designed navigation path while foraging, evading, and attacking, mimicking the air combat scenario.