Vertical takeoff and landing (VTOL) is a core feature of unmanned aerial vehicles (UAVs), which are commonly referred to as drones. In emerging smart logistics, drones are expected to play an increasingly important role as mobile platforms. Therefore, research on last-mile delivery using drones is on the rise. There is a growing trend toward providing drone delivery services, particularly among retailers that handle small and lightweight items. However, there is still a lack of research on a structural definition of the VTOL drone flight model for multi-point delivery service. This paper describes a VTOL drone flight route structure for a multi-drone delivery service using rotary-wing type VTOL drones. First, we briefly explore the factors to be considered when providing drone delivery services. Second, a VTOL drone flight route model is introduced using the idea of the nested graph. Based on the proposed model, we describe various time-related attributes for delivery services using drones and present corresponding calculation methods. Additionally, as an application of the drone route model and the time attributes, we comprehensively describe a simple example of the multi-drone delivery for first-come-first-served (FCFS) services.

This study focuses on the path planning algorithm for large-scale autonomous delivery using drones and robots in urban environments. When generating delivery routes in urban environments, it is essential that avoid obstacles such as buildings, parking lots, or any other obstacles that could cause property damage. A commonly used method for obstacle avoidance is the grid-based A* algorithm. However, in large-scale urban environments, it is not feasible to set the resolution of the grid too high. If the grid cells are not sufficiently small during path planning, inefficient paths might be generated when avoiding obstacles, and smaller obstacles might be overlooked. To solve these issues, this study proposes a method that initially creates a low-resolution wide-area grid and then progressively reduces the grid cell size in areas containing registered obstacles to maintain real-time efficiency in generating paths. To implement this, obstacles in the operational area must first be registered on the map. When obstacle information is updated, the cells containing obstacles are processed as a primary subdivision, and cells closer to the obstacles are processed as a secondary subdivision. This approach is validated in a simulation environment and compared with the previous research according to the computing time and the path distance.

This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.