Uav urban mobility control: swarm intelligence and collision avoidance

Subject matter: Intelligent management of traffic flows in urban environments using swarm intelligence principles and collision avoidance algorithms to ensure safe and efficient urban mobility. Special attention is given to the management of unmanned vehicles and drones. Goal: To develop and analyze an approach to managing urban mobility that combines swarm intelligence principles and collision avoidance algorithms to optimize traffic flows, improve traffic safety, and reduce the number of accidents. Tasks: Investigate the safety and efficiency problems of urban transportation in the context of growing urbanization; develop a model that integrates swarm intelligence and collision avoidance algorithms for managing the movement of unmanned vehicles; conduct a series of experiments to test the effectiveness of the proposed approach; analyze the results of the experiments and determine the potential for improving urban mobility and ensuring road safety. Methods: Mathematical modeling of traffic flows using the swarm intelligence algorithm to coordinate the movement of unmanned vehicles and avoid collisions. Results: The proposed urban mobility management algorithm has demonstrated the ability to improve traffic flows, reduce the risk of collisions, and increase overall road safety. The results of the experiments confirmed the effectiveness of using swarm intelligence for coordination vehicles and collision avoidance algorithms to prevent accidents.