Flight Control Systems for Drones

Gas turbine engine design for aircraft that balances efficiency, operability, and installation benefits. The engine has a fan with a large diameter fan blade height, a compressor with a high pressure ratio and few stages, and a fan speed ratio between 6 and 10. This configuration provides high thermal and propulsive efficiency while reducing susceptibility to rotor bow. The engine core, fan, and gearbox are optimized to achieve these metrics while still allowing for acceptable installation on the aircraft.

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Efficient, safe, and autonomous electrically-powered vertical takeoff and landing (VTOL) aircraft for urban cargo delivery. The aircraft has a tilting tail configuration where the tail can pivot between a forward-flight and hover position. In forward flight, the tail wings provide lift and the tail propellers provide thrust. This reduces drag compared to separate wings and propellers. In hover, the tail wings tilt up to avoid obstacles. The tilting tail allows the aircraft to operate between arbitrary locations without needing dedicated landing pads. Autonomous flight enables cargo delivery without a pilot. The aircraft has heavy cargo capacity, long range, and low noise/emissions compared to helicopters.

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A method for transitioning a VTOL fixed wing aircraft between forward flight and hover using the aircraft's fixed wings to brake during transition. The method involves manipulating angle of attack and forward speed during transition to provide lift and vertical thrust from the wings. This allows controlled flight between forward speed and hover without needing vertical thrust during transition. The high lift, mild stall wings enable the braking effect.

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Thrust generating device for vertical takeoff and landing aircraft that allows precise control of thrust by independently regulating blade pitch and propeller speed. The device has a propeller, motor, actuator, and controller. The controller can either change blade pitch to control thrust while keeping propeller speed constant, or increase propeller speed beyond a reference value to generate higher thrust. This allows fine tuning of vertical lift capability.

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Adjusting thrust demand for multi-rotor aircraft to prevent instability and improve directional control when thrust limits are exceeded. The method involves adjusting the desired thrust for each rotor based on the maximum and minimum thrust demands. If the maximum exceeds the limit, thrust is reduced to avoid overload. If the minimum is below the limit, thrust is increased to prevent underpower. This prevents exceeding thrust limits and improves stability by balancing thrust demands.

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Fully autonomous drone flights where the drone takes off, flies according to a predefined plan, and lands without any further user input beyond an initial command and an endpoint target. The drone uses onboard sensors and mapping to navigate and avoid obstacles. It can also communicate with external systems to gather real-time data for planning purposes. The goal is to enable autonomous drone operations in areas with limited connectivity and for situations where remote control is impractical or unsafe.

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Correcting the pose estimation of a gimbal using a vertically mounted compensation device and a vision sensor. The gimbal's pose is initially estimated using an IMU. The compensation device has its own IMU and a vision sensor. It measures the gimbal pose using the IMU and corrects it using the vision sensor. The vision sensor provides more accurate vertical position information compared to the IMU. This corrected pose is then used for further processing.

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System for wind compensation of an electric aircraft like drones and eVTOLs using a plant model to prevent flight path alteration caused by environmental influences like wind. The system has a sensor on the aircraft to detect geographical data like wind speed and direction. The flight controller uses this data to generate an optimal flight trajectory for the aircraft that compensates for the wind forces. The controller solves an objective function sequentially to generate the wind compensated trajectory. This prevents the aircraft from deviating from its intended path due to wind.

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Method and aircraft for transitioning between vertical takeoff/landing (VTOL) and fixed wing flight regimes in electric vertical takeoff and landing (eVTOL) aircraft. The method involves gradually blending in and out control laws between the VTOL and fixed wing regimes using a state machine implemented by a flight control computer. This allows smooth transitions between the regimes using a subset of actuators in each regime. Conditions like airspeed, attitude, and number of healthy actuators are monitored to enable transitions. A high-level decision maker like a pilot or AI can override the conditions. Blending in and out the control laws over time prevents abrupt changes during transitions.

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Control method for autonomous vehicles to accurately navigate to a target position while accounting for uncertainty in object detection. The method involves estimating the target object's position and attitude multiple times using sensor data, calculating reliability for each estimate based on ideal vs actual point groups, and selecting the most reliable estimate for path planning. This prevents using low confidence detections that could lead to incorrect paths.

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Method for allowing temporarily unmanned aircraft to autonomously avoid hazardous situations and emergencies when the data connection for remote control is unavailable. The method involves the aircraft's onboard systems identifying potential hazards using its own sensors. If a hazard is detected, the aircraft calculates an avoidance route using its own sensors and autonomously executes the maneuvers to avoid the hazard. This allows the aircraft to separate itself from traffic and fly safely in emergencies when the data link is disrupted.

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Determining an optimal flight path for an aerial vehicle based on environmental information and parameters variations for each operation mode. This involves generating candidate paths to the destination, then selecting the optimal path based on factors like environmental data and operation mode variations.

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Flight attitude control system for quadcopter drones that enables multiple flight postures and maneuvers for specific tasks. The system uses a sensor module to acquire flight data, a control module to generate instructions for pitch, roll, and yaw movements, and a motor drive module to actuate the quadcopter's motors based on the control instructions. This allows controlling the quadcopter's attitude beyond just altitude and speed by generating customized movement commands.

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A motion control system for vehicles like drones that dynamically selects the best self-position estimation method based on the vehicle's state and environment. The system has multiple self-position estimation units with varying accuracy. It chooses the appropriate unit based on factors like speed and location. This allows optimized motion control by using high accuracy positioning for critical waypoints and lower accuracy for others.

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Modular UAV flight control system that improves space utilization and reduces weight compared to traditional multi-board flight control systems. The system has separate boards for inertial navigation, atmospheric measurement, main control, data recording, and indicator lights. Each board collects specific data, processes it, and communicates with the others using serial interfaces. This allows independent functionality and upgradeability while maintaining integration.

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A multi-drone coordination system for efficient, intelligent, and reliable cooperative flight and task execution of multiple drones. The system uses optimization algorithms to coordinate and optimize flight missions for multiple drones. It assigns tasks and generates optimal flight paths based on mission requirements and drone status. The drones have flight control units with modules for attitude control, navigation, path planning, and obstacle avoidance. They also have sensors for positioning, environment perception, and collision detection. The system uses wireless networks with high-speed, parallel, reliable, encrypted data transmission for efficient communication.

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Enabling autonomous drones to seamlessly transition between GPS and non-GPS environments without losing position accuracy or stability. The method involves using both GPS and onboard sensors to continuously estimate the drone's position. When GPS is available, it relies on RTK-GPS for high accuracy. In GPS-denied areas, it switches to sensor-based positioning using cameras and IMUs. The drone constantly switches between the two positioning methods based on GPS availability. This allows it to smoothly transition between environments without accuracy degradation or stability issues.

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UAV flight control system that allows precise indoor and GPS-denied outdoor flight using onboard sensors. The system uses computer vision to extract feature points and edges from images captured by the UAV's camera. These points and edges are used to determine the UAV's position indoors or in areas without GPS signals. By relying solely on onboard vision sensors, the UAV can accurately control its flight without external navigation systems.

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Safe flight control for unmanned aerial vehicles (UAVs) using deep learning to prevent collisions and ensure compliance with flight paths. The method involves obtaining motion data and echo signals from the UAV over a time period. This data is used to train a neural network to predict the UAV's position and avoid obstacles based on the initial motion and echoes. The network also checks if the predicted path matches the planned one. If not, it alerts the UAV to correct course to avoid violations. This autonomous collision avoidance and path compliance system uses past motion and sensor data to safely guide UAV flight.

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Controllable intelligent unmanned aerial vehicle (UAV) system that improves the accuracy of planned UAV routes by compensating for errors in attitude parameter estimation. The system uses an inertial navigation module, 3-axis accelerometer, and GPS for attitude and position sensing. A microprocessor connects to all sensors. It compensates for errors between the estimated and measured attitude parameters using a dual-port RAM and feedback control loops. This improves the accuracy of the UAV's planned route by reducing errors in the attitude estimation.

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