Drone Flight Control Precision Enhancements

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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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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A multimodal sensor-based autonomous landing system for aircraft that leverages beacon infrastructure at landing sites along with onboard cameras, ranging radios, and GPS to provide precise, low latency, and robust autonomous landing guidance. The landing system uses multimodal sensing modes including visual, radio ranging, and GPS to accurately localize the aircraft relative to beacons and visual indicators at the landing site. This enables autonomous aircraft to land precisely at designated points in varied conditions using onboard perception sensors and multimodal beacon infrastructure instead of relying solely on GPS.

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Detecting and alleviating the effects of optical discrepancies in images used to guide autonomous navigation by a vehicle such as an unmanned aerial vehicle (UAV). The method involves detecting optical discrepancies caused by issues like dirt on the camera lens. The discrepancies are detected by tracking photometric differences between corresponding pixels in stereo images over time. The discrepancies are used to generate an image mask that ignores error-prone regions. The discrepancies can also trigger cleaning the lens or notifying the user.

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A morphing wing design for aircraft like unmanned aerial vehicles (UAVs) that enables rapid transition between high maneuverability and high speed flight modes. The wing pivots at the aircraft body and rotates the outer section downward and inward. This reduces lift and drag compared to the extended position. The pivoting wing shape creates a fluid channel under it for lift generation. The morphing wing allows quick changes between low drag configurations for fast dives/climbs and high lift configurations for slow flight. This provides enhanced maneuverability and speed for applications like counter-UAS.

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Aerodynamically optimized drone design with one or more fuselages to improve efficiency and stability when flying forward and against headwinds. The drone has two or more fuselages that house the components and propellers. The fuselages can be connected in a catamaran or trimaran configuration. This design allows the drone to achieve maximum aerodynamic efficiency and withstand turbulence when tilted forward at a specific angle during flight.

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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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Aircraft control system that uses a fade function to predict aircraft paths and avoid terrain obstacles. The system takes pilot input commands and attenuates them over time using a fade function. These attenuated commands are then used to predict aircraft paths. If any of the predicted paths intersect with terrain obstacles, the system generates new commands to avoid the obstacle.

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