Tracking aircraft in and near a ramp area is described herein. One method includes receiving camera image data of an aircraft while the aircraft is approaching or in the ramp area, receiving LIDAR/Radar sensor data of an aircraft while the aircraft is approaching or in the ramp area, merging the camera image data and the LIDAR/Radar sensor data into a merged data set, and wherein the merged data set includes at least one of: data for determining the position and orientation of the aircraft relative to the position and orientation of the ramp area, data for determining speed of the aircraft, data for determining direction of the aircraft, data for determining proximity of the aircraft to a particular object within the ramp area, and data for forming a three dimensional virtual model of at least a portion of the aircraft from the merged data.

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 method for providing medical services to a patient, including: receiving a medical service request associated with a patient location; selecting an aircraft, located at an initial location, from a plurality of aircraft based on the patient location and the initial location; determining a flight plan for flying the aircraft to a region containing the patient location; at a sensor of the aircraft, sampling a first set of flight data; at a processor of the aircraft, autonomously controlling the aircraft to fly based on the flight plan and the set of flight data; selecting a landing location within the region; and landing the aircraft at the landing location, including: sampling a set of landing location data; determining a safety status of the landing location based on the set of landing location data; outputting a landing warning observable at the landing location; at the sensor, sampling a second set of flight data; and in response to determining the safety status and outputting the landing warning, autonomously controlling the aircraft to land at the landing location based on the second set of flight data.

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.

Particular embodiments may provide a device capable of autonomous locomotion comprising means for autonomous locomotion, a device controller for navigation of the device by controlling the means for autonomous locomotion, and a host machine running a hypervisor, one or more user virtual machines (UVMs) and a virtual machine controller, wherein the host machine is communicably coupled to the device controller, and wherein at least one of the UVMs provides navigation instructions to the device controller.

A method of computing an aircraft trajectory, between a departure point and an arrival point, comprises: loading the departure point and a departure angle; determining two waypoints; loading the arrival point and an arrival angle; determining two departure circles of respectively left and right type, which are tangent to the oriented departure straight line respectively on the left and on the right, and passing respectively through the waypoint of left type and through waypoint of right type; determining two arrival circles of respectively left and right type which are tangent to the oriented arrival straight line respectively on its left and on its right and passing through the arrival point; determining a plurality of continuous geometric lateral trajectories comprising an initial portion comprising a portion of a departure circle, a final portion equal to a portion of an arrival circle, and an intermediate portion.

Systems, devices, and methods that may include: determining one or more take-off variables for a vertical take-off and landing (VTOL) aerial vehicle; increasing an altitude of the VTOL aerial vehicle to a first altitude, where increasing the altitude comprises substantially vertical flight of the VTOL aerial vehicle; performing a first pre-rotation check of the VTOL aerial vehicle; adjusting a pitch of the VTOL aerial vehicle to a first pitch angle via motor control; adjusting the pitch of the VTOL aerial vehicle to a second pitch angle via at least one of: motor control and one or more effectors; and adjusting the pitch of the VTOL aerial vehicle to a third pitch angle via the one or more effectors, where the third pitch angle is substantially perpendicular to a vertical plane.

Techniques are described for an autonomous asset management system that integrates autonomous devices, such as drone devices and other robotic devices, with a home security system of a property to enable management, monitoring, and/or tracking of various assets located within the property. In some implementations, an indication of an asset associated with a property is obtained by an autonomous device. Sensor data collected by one or more sensors of the property is obtained by the autonomous device based on the indication of the asset. A present status of the asset is determined by the autonomous device based on the sensor data. A determination that the present status of the asset does not correspond to an expected status of the asset is made by the autonomous device. In response, the autonomous device navigates to the particular location of the property.

Aircraft guidance with transmitting beacons is disclosed. An example apparatus includes a transceiver of an aircraft to receive signals from deployed beacons, a signal analyzer to analyze the signals to determine distances of the respective beacons relative to the aircraft, and a position calculator to calculate a positional zone of the aircraft based on the distances.

Various systems and methodologies may be utilized to determine whether a particular shipment/item is eligible for delivery between a manual delivery vehicle and a final destination location via an autonomous delivery vehicle. To ensure autonomous deliveries are performed in a resource effective manner, shipments/items deemed eligible for autonomous delivery may be vetted by comparing the destination for the autonomous delivery shipment/item against one or more manual delivery destinations (serviced by the manual delivery vehicle operator), and ultimately identifying an optimal launch location for the autonomous delivery vehicle to leave the manual delivery vehicle to complete the autonomous delivery. If the autonomous delivery location does not satisfy applicable autonomous delivery criteria, the autonomous delivery shipment/item may be reclassified for manual delivery by the manual delivery vehicle operator.