Abstract
Unmanned aerial vehicles (UAVs) or drones are increasingly used in cities to provide service tasks that are too dangerous, expensive or difficult for human beings. Drones are also used in cases where a task can be performed more economically and or more efficiently than if done by humans. These include remote sensing tasks where drones can be required to form coalitions by pooling their resources to meet the service requirements at different locations of interest in a city. During such coalition formation, finding the shortest path from a source to a location of interest is key to efficient service delivery. For fixed-wing UAVs, Dubins curves can be applied to find the shortest flight path. When a UAV flies to a location of interest, the angle or orientation of the UAV upon its arrival is often not important. In such a case, a simplified version of the Dubins curve consisting of two instead of three parts can be used. This paper proposes a novel model for UAV coalition and an algorithm derived from basic geometry that generates a path derived from the original Dubins curve for application in remote sensing missions of fixed-wing UAVs. The algorithm is tested by incorporating it into three cooperative coalition formation algorithms. The performance of the model is evaluated by varying the number of types of resources and the sensor ranges of the UAVs to reveal the relevance and practicality of the proposed model.
Highlights
Drone technology has recently moved from a niche area mainly controlled by the AmericanDepartment of Defense into a technology that can be accessed off-the-shelf for different purposes and tasks
For each of the optimal coalition formation algorithm (OCFA), polynomial time coalition formation algorithm (PTCFA) and particle swarm optimization (PSO) algorithms, 100 simulations were performed on a square search area with sides of length 1000 m
The positions of the targets were not known to the Unmanned aerial vehicles (UAVs) for both PTCFA and OCFA, while the positions of the targets were known to the UAVs in PSO
Summary
Drone technology has recently moved from a niche area mainly controlled by the American. In 2014, Alec Momont, in the Netherlands, developed an ambulance drone that delivers an automatic external defibrillator to patients via air, much faster than a standard ambulance [22]. This invention could bring relief to many of the approximately 360,000 patients who experienced cardiac arrest in the United States last year. These applications along with the potential for drones to deliver life-saving medicines and supplies to isolated communities in rural and disaster zones when overland access is not an option make the multi-drone task allocation an interesting research area that may benefit both urban and rural areas of the world. Path finding and resources allocation, both aerial and ground-based, are two key challenging issues that need to be addressed in order to provide a team of drones with the necessary autonomy to achieve a task cooperatively
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