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(Sauter) examined the deployment of swarm of small aerial vehicles or agents for search missions in large areas, and found that swarming of small aerial vehicles or agent shares virtual digital pheromone maps, which are used to generate detailed 3D images during search and rescue missions. The small aerial vehicles or agents share a virtual pheromone map showing recently visited areas through higher rates of pheromone. This digital pheromone guides the small aerial agents (or UAVs) to unexplored zones; thus, multi-Agent search strategy using swarm of the small aerial vehicles will leave no area unexplored and this enhances the search and rescue missions [paradzik].
(Albani) introduced the reinforced random walk approach for field coverage and weed mapping by swarming small unmanned aerial vehicles. This strategy includes a study or survey approach, which using a reinforced random walk to detect the availability and the quantity of weeds using swarms of small UAVs. Every specific unmanned aerial vehicle divides the search plane/path into two parts, and explores the preferring to explore the semi-plane ahead based on the utility values.
The angular difference between the direction of scanning and present cell, and momentum of every UAV defines the utility values, which then influences the decision concerning the next cells to the UAV visits. The values are elevated for each location line up with the momentum vector. In addition, the influences from adjacent unmanned aerial vehicles will show the on the executed or completed movements, arbitrarily directing the UAVs to unexplored or poorly explored areas, which is often conducted by computing a repulsion vector.
The swarm of UAVs will exchange data among each other and this prevents covering parts that have been formerly explored. Finally, the swarms of UAVs call together or assemble their members in direction to potential areas and use attraction vector to perform the weed mapping. Based on their study findings,. [Albani] found that swarming multiple UAVs have increases detection efficiency, reduces coverage time and area, and provides detailed field coverage (2D or 3D, as well as 3D mapping or images.
(Di Franco) found that swarming of UAVs or employment of numerous robots in a mission will time-to-completion coverage, reduce the number of turning manoeuvres, minimize energy consumption, and decrease the paths covered (area covered per unit path length travelled), while enhancing coverage precision and offering detailed 2D or 3D images of the area covered. Sensor-based strategies can also be used with swarm of UAVs in missions to obtain real-time information or data such as 3D mapping, and high resolution or high definition images or videos (online or offline) during the mission coverage.
In another study, (Cheng) proposed the use of cooperative Path Planner strategy for unmanned aerial vehicles or small aerial agents that uses Ant Colony Optimization Algorithm with Gaussian distribution functions to deploy swarm of UAVs for intelligent missions in large areas, with the aim of providing high quality 3D images and 3D maps.
(Rosalie) introduced the Chaotic Ant Colony Optimization to Coverage (CACOC) method that integrates Ant Colony Optimization (ACO) with the chaotic dynamical systems, which is used with a Swarm of UAVs for surveillance missions in large areas such as in military setting to provide comprehensive 2D plan and real-time 3D mapping of the surveyed area. The method also enables the ground control station operator to project or predict the paths of the UAVs, whereas keeping it unpredictable to the enemies. Using Chaotic Ant Colony Optimization to Coverage (CACOC) method, the swarm of the UAVs shares a virtual pheromone map that will be used to effectively generate 2D and 3D maps or images.
Problem Statement and Proposed Solution
There are a lot of application that the decision maker need to have a full situation awareness picture about the environment to make sure that he will use the best resources and save the time such as dealing with firefighting in crowded city or surveillance in urban areas. one air plane or drone not enough most of the time to achieve this goal specially if the area of the interest is big, even set of drones can be helpful to enlarge the area of interest but still gives 2D picture about the environment in separate pictures not merged.
The thesis, therefore, proposes the use of set of swarming Arial agents do 3d image coverage in the same time for specific urban area. One 3D merged picture about the environment result from the swarm drones. The swarm drones should coordination between them during the image coverage and keep to the optimum formation to insure enlarge the area of the interest during the swarming.
The study will use use the following drone (MAVIC AIR), as shown in figure 2 below, to achieve the purpose of the study, which is to investigate the 3D coverage by swarming small aerial agents
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