A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

The present disclosure aims to implement UAV (unmanned aerial vehicle) logistics operation and air traffic control in flyable airspace technically through a UAV task planning system, which depends on blockchain technology to carry out UAV air traffic surveillance on flight segments in a predetermined barrier-free airway and optimize air traffic according to a safe separation distance for fewest UAV operators, air traffic controllers, communications links and airborne loads.

Provided is a user equipment for controlling a drone. The user equipment analyzes an original video to control the drone to photograph a reproduction video giving a feeling identical to or similar to the original video. An electronic device may be connected to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, and the like.

A flight plan generation device for generating a flight plan of a flight device that transmits a captured image captured by an imaging unit through wireless communication during flight includes a first acquisition unit configured to acquire target information for specifying an imaging target to be imaged by the imaging unit during flight of the flight device and image quality information for specifying required image quality of a captured image of the imaging target, a second acquisition unit configured to acquire communication quality information on wireless communication quality in a predetermined area including at least a flight airspace of the flight device, and a flight plan generation unit configured to generate a flight plan including a flight path of the flight device and an imaging parameter of the imaging unit based on the acquired target information, image quality information, and communication quality information.

An information processing device determines, based on location information of a plurality of delivery destinations for package delivery: one or a plurality of first delivery destinations for package delivery by a vehicle; and one or a plurality of second delivery destinations for package delivery by a drone mounted on the vehicle. In addition, the information processing device determines: a travel route including a route for the vehicle to perform package delivery to the one or plurality of first delivery destinations; a first point on the travel route for the drone to start flying from the vehicle to the one or plurality of second delivery destinations; and a second point on the travel route for the drone to return to the vehicle.

The present disclosure is directed to systems and methods for trajectory and route planning including obstacle detection and avoidance for an aerial vehicle. For example, an aerial vehicle's flight control system may include a trajectory planner that may use short segments calculated using an iterative Dubins path to find a first path between a start point and an end point that does not avoid obstacles. Then the trajectory planner may use a rapidly exploring random tree algorithm that uses points along the first path as seed points to find a trajectory or route between the start point and end point that avoids known or detected obstacles.

Systems, methods, and computer-readable storage media for generating a flight path for aerial drones around parked vehicles, where the flight path navigates an aerial drone to specific locations associated with predetermined views of the vehicle. A remotely located user can select from a list of predetermined views where the drone should travel, and the computer system can direct the drone to a location associated with the selected view, align the camera on the drone to capture the selected view, and relay live video of the selected view of the parked car to the user.

A method of deploying an unmanned aerial vehicle (UAV) operation system may be provided. A method may include estimating an amount of traffic for one or more routes based on a demand of the one or more routes. The method may also include determining a required number of docking stations for each route of the one or more routes based on the estimated amount of traffic for the route, a distance of the route, and a maximum travel distance for a UAV. Further, the method may include installing the required number of docking stations for each route of the one or more routes, wherein each docking station of the required number of docking stations including at least one of a power supply, a wireless charger, a communication module, a control module, and a camera.

A flight management system for unmanned aerial vehicles (UAVs), in which the UAV is equipped for cellular fourth generation (4G) flight control. The UAV carries on-board a 4G modem, an antenna connected to the modem for providing for downlink wireless RF. A computer is connected to the modem. A 4G infrastructure to support sending via uplink and receiving via downlink from and to the UAV. The infrastructure further includes 4G base stations capable of communicating with the UAV along its flight path. An antenna in the base station is capable of supporting a downlink to the UAV. A control centre accepts navigation related data from the uplink. In addition, the control centre further includes a connection to the 4G infrastructure for obtaining downlinked data. A computer for calculating location of the UAV using navigation data from the downlink.

A method, medium, and system to receive flight parameter data relating to a plurality of flights, the flight parameter data including indications of aircraft performance based navigation (PBN) capabilities, flight plan information, an aircraft configuration, and an airport configuration for the plurality of flights; assign probabilistic properties to the flight parameter data; receive accurate and current position and predicted flight plan information for a plurality of aircraft corresponding to the flight parameter data; determine a probabilistic trajectory for two of the plurality of aircraft based on a combination of the probabilistic properties of the flight parameter data and the position and predicted flight plan information, the probabilistic trajectory being specific to the two aircraft and including a target spacing specification to maintain a predetermined spacing between the two aircraft at a target location with a specified probability; and generate a record of the probabilistic trajectory for the two aircraft.