Described are systems and methods for magnetically assisted boom refueling. In certain examples, a magnetic refueling receiver is disclosed that includes a refueling receptacle configured to receive a portion of a refueling boom and a receptacle magnet disposed around at least a portion of a perimeter of the refueling receptacle. In another example, a magnetic refueling boom is disclosed that includes a refueling boom structure that includes a first end configured to be inserted into a refueling receiver and a pipe magnet disposed around at least a portion of a perimeter of the refueling boom structure.

Described are systems and methods for magnetically assisted boom refueling. In certain examples, a magnetic refueling receiver is disclosed that includes a refueling receptacle configured to receive a portion of a refueling boom and a receptacle magnet disposed around at least a portion of a perimeter of the refueling receptacle. In another example, a magnetic refueling boom is disclosed that includes a refueling boom structure that includes a first end configured to be inserted into a refueling receiver and a pipe magnet disposed around at least a portion of a perimeter of the refueling boom structure.

A referencing system to assist a receiver aircraft in relative positioning during in-flight refueling operation that includes an array of references congregated on a spot of the tanker aircraft, wherein the array of references provide a distinguishable visual indicator depending on the sector where the receiver aircraft positions.

A referencing system to assist a receiver aircraft in relative positioning during in-flight refueling operation that includes an array of references congregated on a spot of the tanker aircraft, wherein the array of references provide a distinguishable visual indicator depending on the sector where the receiver aircraft positions.

Aspects of the disclosure provide solutions for determining a position of an object in a video frame. Examples include: receiving a segmentation mask of an identified object in a video frame; adjusting a 3D representation of a moveable part of the object based on constraints for the moveable part; comparing the 3D model of the object to the segmentation mask of the object; determining a match between the 3D model of the object to the segmentation mask of the object is above a threshold; and based on the match being above the threshold, determining a position of the object.

Nuclear Aircraft Transportation System “KARAVAN” with its components is represented by a group of inventions in the technical and organizational relations. The main and basic invention is Nuclear Aircraft Transportation System “KARAVAN” (NATS). This invention includes two other ones: Aircraft Thrust Nuclear Power Plant, (ATNPP), which in turn includes—Thermal Power Cycle of ATNPP, (TPC ATNPP). In addition, the represented group of inventions is made up of two more inventions: Maintenance System of ATNPP, (MS ATNPP) and Emergency Response System of NATSK, (ERS NATSK).

The concept of practical implementation of the presented group of inventions involves the fact that ATNPP, which is a large unmanned drone aircraft “Tiagach”, supplies the aero-train composed of a number of passenger liners and cargo transport planes using electric motors with traction electric energy in the air.

The power supply of such an aero-train is based on the onboard Nuclear Power Plant of the aircraft “Tiagach”. In this case, the transmission of electric power to the towed electric aircraft of the aero-train is carried out by means of electric split feeders and cables, connecting and disconnecting of which between airplanes of the aero-train is carried out in the air, by analogy with refueling of airplanes in the air with JP fuel.

During the flight of the aero-train on a logistically optimized route, electric airplanes can detach from and attach to the aero-train, taking off and landing along the flight route of the aero-train using their own electric accumulators. In addition, extra ATNPP may be included in the aero-train during its flight, if it is necessary to increase the thrust. At the same time, due to the use of nuclear power, such ATNPP can remain in the air for a conditionally indefinite period of time.

The invention is aimed at creating cost-effective air freight and passenger traffic.

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/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.

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.