An obstacle avoidance method is applicable to an unmanned aerial vehicle (UAV). The UAV includes binocular cameras. The the obstacle avoidance method includes: acquiring a binocular direction corresponding to each binocular camera, each binocular direction being corresponding to obstacle sectors; detecting an obstacle distance of each of obstacle sectors corresponding to each binocular direction; determining an obstacle distance in each binocular direction according to the obstacle distance of each of obstacle sectors corresponding to each binocular direction; and determining an obstacle avoidance policy according to the obstacle distance in each binocular direction with reference to a flight direction of the UAV. By determining the obstacle distance in each binocular direction, and then determining the obstacle avoidance policy with reference to the flight direction of the UAV, the obstacle avoidance success rate of the UAV is improved.

A UAV including a first barometer and a processing unit is provided. The first barometer provides a first air pressure value. The processing unit is coupled to the first barometer for receiving the first air pressure value from the first barometer, performing timing-synchronization on the first air pressure value provided by the first barometer and an external reference air pressure value provided by an external reference barometer to obtain a timing-synchronized first air pressure value and recalculating the timing-synchronized first air pressure value to generate a compensated air pressure value, wherein the processing unit performs data fusion calculation on the first air pressure value, the compensated air pressure value and a sensor data to obtain a target fused data and real-timely controls the altitude and the posture of the UAV according to the target fused data.

Embodiments described herein may help to provide support via a fleet of unmanned aerial vehicles (UAVs). An illustrative medical-support system may include multiple UAVs, which are configured to provide support for a number of different situations. Further, the medical-support system may be configured to: (a) identify a remote situation, (b) determine a target location corresponding to the situation, (c) select a UAV from the fleet of UAVs, where the selection of the UAV is based on a determination that the selected UAV is configured for the identified situation, and (d) cause the selected UAV to travel to the target location to provide support.

A system for performing a collaborative and smart mission assigned over a determined area includes: a plurality of land bases that communicate with one another by communication, analysis, and/or examination means; at least one satellite drone associated with each base of the plurality of land bases, each satellite drone of the at least one satellite drone performing the assigned mission or part of the assigned mission, such that different drones of the at least one satellite drone form a flotilla whose members communicate with each other and directly or indirectly with the plurality of land bases by communication, analysis, and/or examination means respectively assigned to relations of the different drones with each other and of the plurality of land bases with the different drones. The mission is assigned by the plurality of land bases to the flotilla of drones in one or several determined zones.

Apparatus and methods for controlling unmanned systems (UMSs), such as unmanned aircraft, are provided. A UMS can be provided that includes a physical computer, one or more auxiliary systems for the UMS, and a payload. The physical computer can execute software to cause the physical computer at least to instantiate a plurality of virtual computers that include a mission virtual computer and a payload virtual computer for: controlling the one or more auxiliary systems for the UMS using the mission virtual computer, communicating with the payload using the payload virtual computer, determining whether a software fault has occurred on one virtual computer of the plurality of virtual computers, and after determining that a software fault has occurred on one virtual computer of the plurality of virtual computers, preventing the software fault from causing a fault on a different virtual computer of the plurality of virtual computers.

A method for dynamically controlling the attitude of a rotary-wing drone. The method includes dynamically controlling the attitude of the drone when the drone is flying using lift of each of four wings of the drone, by controlling the attitude of the drone by sending differentiated commands to one or more propulsion units of the drone so as to rotate the drone about a roll axis and/or pitch axis and/or heading axis of the drone from a current angular position to a final angular position, the axes being defined in the reference point of the drone.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ground control point assignment and determination. One of the methods includes receiving information describing a flight plan for the UAV to implement, the flight plan identifying one or more waypoints associated with geographic locations assigned as ground control points. A first waypoint identified in the flight plan is traveled to, and an action to designate a surface at the associated geographic location is designated as a ground control point. Location information associated with the designated surface is stored. The stored location information is provided to an outside system for storage.

Disclosed is a drone. The present invention includes a plurality of propellers creating a lift to prevent inclination and overturn of the drone due to a lift difference generated from uneven ground, a power driving unit providing a rotation power to each of a plurality of the propellers, a ground sensing unit measuring a distance to a first region of the ground and a shape of the first region, and a controller controlling the power driving unit to differentiate rotation ratios of a plurality of the propellers based on the measured distance and shape if receiving an input signal for landing at the first region.

Obstruction detection and management systems and methods are performed through an Air Traffic Control (ATC) system for Unmanned Aerial Vehicles (UAVs). The obstruction detection and management method includes receiving UAV data from a plurality of UAVs, wherein the UAV data includes operational data for the plurality of UAVs and obstruction data from one or more UAVs; updating an obstruction database based on the obstruction data; monitoring a flight plan for the plurality of UAVs based on the operational data; and transmitting obstruction instructions to the plurality of UAVs based on analyzing the obstruction database with their flight plan.

An Unmanned Aerial Vehicle (UAV) air traffic control method utilizing wireless networks and concurrently supporting delivery application authorization and management communicating with a plurality of UAVs via a plurality of cell towers associated with the wireless networks, wherein the plurality of UAVs each include hardware and antennas adapted to communicate to the plurality of cell towers; maintaining data associated with flight of each of the plurality of UAVs based on the communicating; processing the maintained data to perform a plurality of functions associated with air traffic control of the plurality of UAVs; and processing the maintained data to perform a plurality of functions for the delivery application authorization and management for each of the plurality of UAVs.