Autonomous Delivery Drones Powered by AI for Urban Logistics Using a Pathfinding and Simultaneous Localization and Mapping

Modern urban logistics is experiencing significant development, and solving the key questions like traffic problems, delivery time, and emissions became crucial. Self-driving drones that employ AI technology can become a perfect solution for solving the problems of efficient city logistics. The principal area of interest in this research is to employ AI-based path planning algorithms and incorporate SLAM techniques into delivery drones to optimize their performance within UAS's environments. Using path planning techniques propelled by Artificial Intelligence, drones can modify their operating pathways depending on real challenges that may include physical obstacles, weather disturbances or even traffic jams in recognition of an optimum safe path of operation. While, SLAM also helps the drones to provide self-localization and mapping in the urban environments without relying on GPS signal and increase the level of accuracy and operation freedom. This paper focuses on the combined use of path planning algorithms and SLAM in autonomous delivery drones and work towards understanding the capability that may reshape the future of logistics. Also included in this work is the strategy of bringing the focus to the question of how this system can be scaled while looking at best practices in terms of its operation, for instance how efficient the use of energy is, how much the system costs and how existing legislation will impact the system. These findings prove the effectiveness of enabling autonomous drones for last-mile delivery and add to the concept of urban fulfillment and smart city infrastructure.