An aircraft position control system includes a feedback control unit configured to calculate a feedback manipulated variable of an aircraft by feedback control so that the aircraft heads toward a target landing point, based on at least the relative position and the relative velocity; a multi-value control unit configured to set, by referring to a switching line preliminarily provided in a manner passing through an origin of a coordinate plane whose orthogonal axes represent the relative position and the relative velocity and separating an acceleration region in which the relative velocity is to be increased and a deceleration region in which the relative velocity is to be decreased, an addition value that tends to increase the relative velocity when a coordinate point of the current relative position and the current relative velocity is located in the acceleration region to calculate a manipulated variable of the aircraft

An aircraft position control system includes a feedback control unit configured to calculate a feedback manipulated variable of an aircraft by feedback control so that the aircraft heads toward a target landing point, based on at least the relative position and the relative velocity; a multi-value control unit configured to set, by referring to a switching line preliminarily provided in a manner passing through an origin of a coordinate plane whose orthogonal axes represent the relative position and the relative velocity and separating an acceleration region in which the relative velocity is to be increased and a deceleration region in which the relative velocity is to be decreased, an addition value that tends to increase the relative velocity when a coordinate point of the current relative position and the current relative velocity is located in the acceleration region to calculate a manipulated variable of the aircraft

An unmanned aerial vehicle (UAV) has a multicopter section for flying in air with an attached blower section for generating an air stream for blowing dust off surfaces. A flight controller controls the multicopter section, a blower controller controls the blower section, and a power supply supplies power to the multicopter and blower sections. The flight controller and the blower controller are connected, and the blower controller is adapted to supply blower control commands to the flight controller to compensate for the thrust of the air stream from the blower section by flight control of the multicopter section. The UAV may be enclosed by a protective cage in the form of a meshed polyhedron, wherein the rods of the meshes are elastically connected at the respective nodes.

An unmanned aerial vehicle (UAV) has a multicopter section for flying in air with an attached blower section for generating an air stream for blowing dust off surfaces. A flight controller controls the multicopter section, a blower controller controls the blower section, and a power supply supplies power to the multicopter and blower sections. The flight controller and the blower controller are connected, and the blower controller is adapted to supply blower control commands to the flight controller to compensate for the thrust of the air stream from the blower section by flight control of the multicopter section. The UAV may be enclosed by a protective cage in the form of a meshed polyhedron, wherein the rods of the meshes are elastically connected at the respective nodes.

Systems and methods for calibrating a synthetic image on an avionic display. The method includes receiving a synthetic image frame generated by an avionic display system and a sensor image frame from a forward-facing onboard sensor system. Object recognition is performed on the sensor image frame to generate a first search region for a first Federal Aviation Administration (FAA) marking, defined as a first search region-FAA marking pair. The sensor image frame and the first search region-FAA marking pair are processed using a deep learning method (DLM) to determine for the first FAA marking of the first search region-FAA marking pair, each of: a position deviation calculation, an orientation deviation calculation, and a synthetic image distortion factor. The synthetic image frame is then updated and rendered in accordance with the position deviation calculation, the orientation deviation calculation, and the synthetic image distortion factor, thereby calibrating the synthetic image frame.

Systems and methods for calibrating a synthetic image on an avionic display. The method includes receiving a synthetic image frame generated by an avionic display system and a sensor image frame from a forward-facing onboard sensor system. Object recognition is performed on the sensor image frame to generate a first search region for a first Federal Aviation Administration (FAA) marking, defined as a first search region-FAA marking pair. The sensor image frame and the first search region-FAA marking pair are processed using a deep learning method (DLM) to determine for the first FAA marking of the first search region-FAA marking pair, each of: a position deviation calculation, an orientation deviation calculation, and a synthetic image distortion factor. The synthetic image frame is then updated and rendered in accordance with the position deviation calculation, the orientation deviation calculation, and the synthetic image distortion factor, thereby calibrating the synthetic image frame.

Systems, methods, and devices described in the present disclosure provide technology for detecting features of interest depicted in a video stream supplied by a camera-equipped drone and adjusting the flight pattern of the drone to increase the amount of time in which the features of interest are within the line of sight of the drone. In addition, the present disclosure provides technology for detecting when events of interest occur at the features of interest and alerting personnel when the events occur. In some examples, the technology of the present disclosure also determines a format for an alert (e.g., a push notification) based on an event type so that personnel who receive the alert are apprised of the urgency of the event based on the format.

Systems, methods, and devices described in the present disclosure provide technology for detecting features of interest depicted in a video stream supplied by a camera-equipped drone and adjusting the flight pattern of the drone to increase the amount of time in which the features of interest are within the line of sight of the drone. In addition, the present disclosure provides technology for detecting when events of interest occur at the features of interest and alerting personnel when the events occur. In some examples, the technology of the present disclosure also determines a format for an alert (e.g., a push notification) based on an event type so that personnel who receive the alert are apprised of the urgency of the event based on the format.

An unmanned aerial system includes an unmanned aerial vehicle having a body and a primary propulsion system coupled to the body. The primary propulsion system includes at least one propeller and at least one motor coupled to the at least one propeller. The unmanned aerial system also includes a pair of landing gears coupled to the body of the unmanned aerial vehicle. Each landing gear of the pair of landing gears includes a buoyant elongated float. The unmanned aerial system also includes a SONAR device coupled to the unmanned aerial vehicle.

An unmanned aerial system includes an unmanned aerial vehicle having a body and a primary propulsion system coupled to the body. The primary propulsion system includes at least one propeller and at least one motor coupled to the at least one propeller. The unmanned aerial system also includes a pair of landing gears coupled to the body of the unmanned aerial vehicle. Each landing gear of the pair of landing gears includes a buoyant elongated float. The unmanned aerial system also includes a SONAR device coupled to the unmanned aerial vehicle.