Flight Control Systems for Drones

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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