Solving Vehicle Routing Problem for Intelligent Systems using Delaunay Triangulation

Intelligent Systems are of many types. This work focuses mainly about the autonomous drones and UAVs. Only for military objectives were Unmanned Aerial Vehicles (UAV) invented and developed. They have lately increased in use, nevertheless, in the civil applications this study shall examine. This technology has a wide range of applications, including in agriculture, environmental protection, public safety, and traffic flow management. The use of unmanned aerial vehicles (UAVs) in smart cities is another promising new use for this technology. By 2050, the world's population is expected to have doubled. As a result, towns and communities face new difficulties and possibilities as a result of these expectations. For any smart city design, efficiency and cost-effectiveness are the primary objectives. After the global economic downturn, people's curiosity for smart cities has grown exponentially. Unmanned aerial vehicles' (UAVs') to discover the optimal route between a source and a destination, vehicle routing is a crucial field for study. There are a number of crucial considerations to be made in order to address these challenges in vehicle routing for UAVs. UAVs must be able to see where they are in relation to the map or graph in order to make these judgments. Planned routes for unmanned aerial vehicles are not only designed to minimize the risk of collisions, but they are also designed to be as efficient as possible. To get at the end destination in the least amount of time feasible, solution for vehicle routing problems is essential. In this work, we provide a solution to the vehicle routing issue in the form of an algorithm. The use of Delaunay triangulation is done to achieve the shortest path for the intelligent systems from source to destination. The experimental findings demonstrate that the Lin-Kernighan technique performs better than alternative algorithms for addressing the vehicle routing issue.