In various aspects, a first set of attributes associated with a target location are determined. The target location is a location that one or more items are to be delivered to by an unmanned aerial vehicle (UAV). The first set of attributes are compared with a second set of attributes. The second set of attributes indicate attributes of each UAV of a plurality of UAVs. Based on the comparing, a UAV, of the plurality of UAVs, is recommended to deliver the one or more items to the target location.

A formation flight assistance system is placed on board a follower aircraft to benefit from an upwards air flow induced by a wake vortex generated by a leader aircraft. The system determines a wake vortex effect experienced by the follower aircraft as being a difference between measurements taken by sensors and modelling in a wake vortex free environment. Using a recursive Bayesian filter, the system determines an estimated position of the wake vortex on the basis of information relating to the leader aircraft and a wake vortex model, and determines an estimation uncertainty, according to which a potential discomfort window is computed for the passengers of the follower aircraft. The system keeps the follower aircraft outside the potential discomfort window. Thus, the comfort of the passengers of the follower aircraft is provided, while benefiting from an upwards air flow induced by the wake vortex.

A central management server includes a determination module determining a landable local delivery hub among a plurality of local delivery hubs located within a preset radius centered around a destination, when a location information request for the landable local delivery hub is received from an unmanned cargo aircraft; and a control module configured to transmit a landing command including the location information of the determined landable local delivery hub to the unmanned cargo aircraft, and transmit a task execution command to cause the unmanned cargo aircraft to deliver goods to the destination. The determination module determines the landable local delivery hub, based on a combination of an expected landing standby period of the unmanned cargo aircraft, an expected battery consumption amount of the unmanned cargo aircraft, an expected delivery period of the unmanned delivery robot, and an expected battery consumption amount of the unmanned delivery robot.

An architecture for monitoring at least one aircraft. The architecture comprises an avionics system configured to generate avionics data during use of the aircraft; a mobile electronic device including an analysis unit configured to convert at least one maintenance operation into operational data; and an alerter configured to display at least one item of monitoring information; and a cloud computing infrastructure. The analysis unit and the alerter are configured to implement a local operating mode and an operating mode connected to the cloud computing infrastructure.

A device includes a processor. The processor is configured to execute instructions to: receive a request from an application to subscribe to a telemetry messaging service; grant a subscription to the telemetry messaging service, to the application based on the request; receive telemetry messages from drones over a radio access network (RAN); process the telemetry messages; and provide the processed telemetry messages to the application over the RAN.

Aspects of the disclosure provide fuel receptacle position/pose estimation for aerial refueling (derived from aircraft position and pose estimation). A video frame, showing an aircraft to be refueled, is received from a single camera. An initial position/pose estimate is determined for the aircraft, which is used to generating an initial rendering of an aircraft model. The video frame and the initial rendering are used to determining refinement parameters (e.g., a translation refinement and a rotational refinement) for the initial position/pose estimate, providing a refined position/pose estimate for the aircraft. The position/pose of a fuel receptacle on the aircraft is determined, based on the refined position/pose estimate for the aircraft, and an aerial refueling boom may be controlled to engage the fuel receptacle. Examples extract features from the aircraft in the video frame and the aircraft model rendering, and use a deep learning neural network (NN) to determine the refinement parameters.

Aspects of the disclosure provide fuel receptacle position estimation for aerial refueling (derived from aircraft position estimation). A video stream comprising a plurality of video frames each showing an aircraft to be refueled, is received from a single camera. An initial position estimate is determined for the aircraft for the plurality of video frames, generating an estimated flight history for the aircraft. The estimated flight history for the aircraft is used to determine a temporally consistent refined position estimate, based on known aircraft flight path trajectories in an aerial refueling setting. The position of a fuel receptacle on the aircraft is determined, based on the refined position estimate for the aircraft, and an aerial refueling boom may be controlled to engage the fuel receptacle. Examples may use a deep learning neural network (NN) or optimization (e.g., bundle adjustment) to determine the refined position estimate from the estimated flight history.

Methods and apparatus are provided for allocating tasks to be performed by one or more autonomous vehicles to achieve a mission objective. Generally, a task allocation system identifies a final task associated with a given mission objective, identifies predecessor tasks necessary to complete the final task, generates one or more candidate tasks sequences to accomplish the mission objective, generates a task allocation tree based on the candidate task sequences, and searches the task allocation tree to find a task allocation plan that meets a predetermined selection criteria (e.g., lowest cost). Based on the task allocation plan, the task allocation system determines a task execution plan and generates control data for controlling one or more autonomous vehicles to complete the task execution plan.

A power recharging system may include an electrical conductor connected to a first aircraft and configured to connect to a second aircraft while the aircrafts are in flight. An AC signal may be induced in the electrical conductor it is in proximity to a changing magnetic field. The system may include a first rectifier circuit at the first aircraft that converts the AC signal into a DC signal for charging the first aircraft and a second rectifier circuit at the second aircraft that converts the AC signal into a second DC signal for charging the second aircraft. The electrical conductor may be part of a communication line. A communication system at the first aircraft may send and receive data communications via the communication line.

Aspects of the disclosure relate to identifying and responding to problem conditions for a fleet of aerial vehicles. This may include receiving at one or more processors of one or more server computing devices sensor feedback from an AV of the fleet. A problem condition may be identified using the sensor feedback. A mitigation response for the problem condition relating to a mission assigned to the aerial vehicle may be determined. The mitigation response may be sent to the AV in order to cause the aerial vehicle to maneuver according to the mitigation response and thereby automatically respond to the problem condition.