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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Control system and method for fixed-wing unmanned aerial vehicles that improves autonomy and stability by offloading computationally intensive tasks from the flight control computer to an onboard processor. The system has an acquisition system to collect environmental, terrain, and obstacle data. An onboard processor processes the data and makes decisions based on ground station instructions. It sends instructions to the flight control computer rather than the limited flight computer doing all processing. This allows more complex autonomous tasks without overloading the flight computer.

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Method for controlling an unmanned aerial vehicle (UAV) to safely fly around no-fly zones. The method involves determining maneuverability limits for the UAV based on its current position and speed relative to nearby no-fly zones. It does this by identifying restricted areas outside the no-fly zone and determining speed and attitude limits within those areas. This allows the UAV to dynamically adjust its flight parameters to avoid violating the limits as it approaches and leaves the no-fly zone.

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Autonomous flight control system for unmanned aerial vehicles (UAVs) that allows the UAV to fly autonomously without human intervention after takeoff. The system uses an onboard microprocessor, wireless communication module, multiple sensors, and actuators to convert high-level flight instructions and sensor data into low-level channel values for the flight control board. This enables autonomous missions like automatic takeoff, navigation, fixed altitude flight, autonomous path planning, target search, and landing. The microprocessor converts the instructions into channel values and sends them to the flight control board over S-Bus protocol. This allows the UAV to continue flying or return after takeoff without remote control input.

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Method for optimizing the optimal route for UAV flight in complex environments that enables efficient, safe, real-time path planning and obstacle avoidance in complex environments while being able to quickly adapt to environmental changes and provide effective response mechanisms in emergency situations. The method uses multi-sensor data fusion and deep learning algorithms to obtain a comprehensive and accurate environment model. It also has an emergency response module with predetermined lowest-risk paths or safe area guides to handle unexpected events like signal loss, unknown objects, or extreme weather.

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Fully autonomous drone flights that allow safe and easy operation without the need for remote control devices. The drones receive an initial flight command and a destination object/hand to land on. They then autonomously navigate to the destination using onboard sensors and processing. This allows safe and reliable flight without the complexity and latency of remote control. The drones can also capture images/video during flight using local image processing to perform tasks like facial recognition, emotion transfer, and style transfer.

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Fully autonomous drone flights where the drone takes off, follows a predefined flight plan, and lands without any further user input other than an initial command and a destination. The drone uses onboard sensors and mapping to navigate the flight plan. At the end, it lands on the indicated object or hand. This allows fully autonomous drone flights that are safe, convenient, and don't require complex remote control. The drone receives an initial flight command and destination, then autonomously executes the flight plan.

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Remote pilot control of an electric aircraft during autopilot using a flight controller to determine user authority level and issue appropriate commands. The flight controller receives control inputs from a remote device and compares them against thresholds to determine full, partial, or no control authority. It then generates commands for the aircraft's actuators based on the authorized level.

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A vertical takeoff and landing (VTOL) aircraft capable of transitioning from vertical to horizontal flight configurations for improved efficiency and ease of use compared to traditional aircraft. The aircraft has multiple rotors on the main and vertical wings that can adjust speed and pitch independently to provide complete control and rotation about any axis. It uses electric motors for propulsion and a flight control system that allows semi-autonomous flight with simple directional commands. This allows the aircraft to take off and land vertically like a helicopter but transition to horizontal flight like a fixed-wing aircraft for faster speeds and longer range.

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Controlling unmanned vehicles to prevent collisions and reduce user burden when multiple vehicles are operated. It provides autonomous control for commercial off-the-shelf unmanned vehicles via an add-on controller that receives user inputs and generates modified control signals to instruct the vehicles to follow pre-determined routes. The controller analyzes the user inputs and extracts the intended maneuvering commands while discarding velocity changes. To avoid collisions, the routes are generated by a server based on sensor data and deconflicting with other vehicles.

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Enhancing the safe and reliable flight control of unmanned aerial vehicles (UAVs) by intelligently switching control devices when needed. The method involves detecting when a UAV requires a control device switch, such as due to illegal flight behavior or communication issues. It leverages preconfigured or dynamically obtained UAV management strategies to decide when to switch control devices and to which ones. The switching can be triggered by the UAV itself or an external entity like a UTM. This intelligent control device switching strategy helps ensure effective UAV control and avoid accidents.

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Updating delivery flight plans for unmanned aerial vehicles (UAVs) to allow changes to orders in progress. The system receives order updates while a UAV is en route, determines if the flight plan needs changing, and updates it accordingly. This allows modifications like adding/removing items, changing delivery locations, or canceling orders. The UAV can return to base, add waypoints, or adjust routes as needed. It increases flexibility and efficiency by avoiding wasted trips due to unmodifiable flight plans.

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Centralized management of a plurality of unmanned aerial vehicles (UAVs) of different types for preventing complications of processing related to information acquisition from each UAV, even in a case where flight management of a plurality of types of UAVs different from each other is performed. The technique involves using an information communication device attached to the UAVs that acquires flight status information and transmits it wirelessly in a predetermined data format. A centralized management device receives and monitors the UAVs using the flight status information transmitted from the information communication device. This standardized format allows uniform acquisition of flight status from different UAV types.

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Path planning system for unmanned aerial vehicles (UAVs) that considers local wind and turbulence conditions. The system estimates wind/turbulence distributions at a given altitude and generates a cost map. This cost map is then used during path planning to calculate flight paths that avoid areas with high wind/turbulence.

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Air traffic control system for managing unmanned aerial vehicles (UAVs) using existing wireless networks like cell networks to enable safe drone delivery operations. The system monitors and controls UAV flights, provides navigation assistance, collision avoidance, switchover between networks, and airspace coordination. The system uses wireless networks to communicate with UAVs and leverages network coverage, bandwidth, and redundancy for air traffic management.

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Cooperative control system for obstacle avoidance in drones that allows autonomous flight around obstacles without human intervention. The system has an inner loop control for real-time obstacle detection and avoidance, and an outer loop control for path planning and high-level maneuvering. The inner loop uses onboard sensors to identify obstacles and calculate safe speeds for the drone's rotors to avoid collisions. The outer loop uses physical relationships to calculate expected forces, angles, and rotor speeds for a clear path around obstacles. The inner and outer loops cooperate to fly the drone safely around obstacles while completing its mission.

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A drone that can be controlled over a mobile network using Internet protocols instead of a limited-range radio-frequency controller. The drone has a network adapter that allows it to receive commands over the mobile network and actuate its functions accordingly. This enables extended range drone operations beyond the limits of a radio controller.

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Reinforcement learning is used to generate optimal operation plans for multi-drone networks performing cooperative tasks like data sensing and communication relays. The approach trains actor neural networks for each drone to learn policies for state-based decision-making. These policies are then used to generate a plan by simulating the game state, obtaining observations, inferring actions, and recording the history. The resulting plan provides coordinated task execution that maximizes efficiency while maintaining network connections.

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Enabling safe, efficient, and uninterrupted control of unmanned aerial vehicles (UAVs) over commercial cellular networks like 4G. It allows using existing cellular infrastructure for UAV flight control and data transfer. The key is using dual antennas on the UAV, one omnidirectional and one unidirectional, to maintain a continuous wireless connection. The Omni antenna scans for the strongest cellular signal, and then the unidirectional antenna points at that cell tower. This enables seamless handover between towers as the UAV flies. Other features include multiple SIM cards for redundancy, QoS guarantees for flight control data, and specialized UAV network operators.

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An unmanned aerial vehicle (UAV) that can autonomously fly, detect objects, avoid collisions, and land safely. The UAV has sensors like a camera and radar to detect objects and a controller that analyzes the sensor data to determine if an override condition exists. If so, it enters a hover mode where it hovers in place for remote control.

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Safely determining the flight path of an unmanned aerial vehicle using multiple position determination systems to provide redundancy and error checking. The method involves using a primary position system like GPS along with a secondary position system like image-based or lidar to independently determine the vehicle's position. Then, a plausibility check is performed by comparing the two sets of position data. If they pass the check, the primary position is used. This allows reliable flight path determination even if the primary system fails or has errors.

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An unmanned aircraft can fly stably and carry cargo. The aircraft has at least two generators, each with a rotor blade and a sensor to detect tilt. The aircraft controls flight using the generators. When loaded with cargo, an output force trigger causes the generators to operate individually until the tilt reaches a threshold. A reference output force is determined based on the individual generator values and positions. This reference is used to control the aircraft for stable flight even with imbalanced cargo.

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Using machine learning to guide unmanned aerial vehicles (UAVs) and other platforms around obstacles without expensive imaging systems. The approach involves training ML models to generate guidance information like object locations based on time-of-arrival (TOA) data from sensors. This avoids the computational expense of processing images to identify obstacles in real time on board the platform.

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Use of autonomous vehicles like UAVs for capturing images with intuitive high-level control objectives that abstract away the complexity of autonomous flight. Developers can use an API to specify objectives like tracking objects, capturing interesting scenes, etc. These objectives are used by the autonomous navigation system for flight planning. Developers can also build skills that modify objectives and control the vehicle behavior during flight. The skills can be shared, learned from, and used for visual outputs like tracking and recognition to enhance autonomous performance.

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Flight control system for unmanned aerial vehicles that enables safe passing of nearby aircraft. The system detects nearby aircraft and determines if passing is possible based on their trajectories and airspace conditions. If passing is possible, it controls the drone to perform a passing maneuver at a safe distance from the other aircraft.

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Navigation system for unmanned aerial vehicles (UAVs) that does not rely on GPS and can work in urban environments with limited satellite coverage. The system uses two synchronized periodic wideband signals transmitted from base stations. The UAV receives the signals and determines its position relative to the base stations based on the signal reception times and intensities. This allows the UAV to navigate along a flight path defined by the base stations without GPS.

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Controlling an unmanned aerial vehicle (UAV) to automatically assist in navigating over sloping terrain. The technique involves using the UAV's sensors to measure distances to the ground and determine the slope angle. The flight control system then adjusts the vertical and horizontal thrust to move the UAV along the slope. This allows the UAV to autonomously climb or descend slopes without requiring precise manual control inputs.

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A flight route splicing method for unmanned aerial vehicles to enable efficient multi-route planning using smart algorithms. The method involves obtaining two flight routes, determining an intermediate route between them based on waypoint elevations, and splicing the routes to generate a composite route. This allows the UAV to fly the routes consecutively without returning to a base, improving efficiency by enabling execution of multiple routes in a single flight.

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System for mid-air collision avoidance and traffic control between unmanned aerial platforms with different priority levels. The system involves CAS (collision avoidance system) units on each platform that intermittently transmit their locations. Higher priority platforms can receive these transmissions and calculate collision risks. If risk is high, the higher priority platform CAS unit generates and transmits steering commands to the lower priority platform to avoid collision.

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Detecting connectivity anomalies in a flight area to ensure safe operation of unmanned aerial vehicles (UAVs) in beyond-line-of-sight applications. The method involves acquiring actual connectivity measurements at various locations within the flight area, comparing them against predicted connectivity data, and identifying deviations as anomalies. These anomalies are reported to aviation control centers to adjust UAV guidance.

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A gesture-based flight control system for unmanned aircraft that allows simplified, intuitive control using hand gestures for tasks like takeoff and landing. The system uses image recognition to identify and track user hand gestures in images captured by the aircraft's camera. It generates flight control commands based on recognized gestures. The aircraft follows the user's movements and gestures to perform flight maneuvers like takeoff, landing, and following the user.

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