Protective Controls in Drone Operation

Aircraft system to provide pilots with real-time feedback on remaining control margins to prevent overloading the aircraft's systems. It calculates the range of effective input commands from the control device that won't saturate the aircraft's effectors. This range is displayed to the pilot, showing how much leeway they have before reaching limits. It uses aircraft state, effector limits, and current actuation to determine the effective input range.

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A vertical takeoff and landing (VTOL) aircraft with electric propulsion systems that have features to improve performance, efficiency, and safety. The electric engines have reduced oil volumes to eliminate fire hazards. The engines also use air cooling and cooling mixtures to manage heat. The engines are connected directly to the fuselage and have integrated inverters. The aircraft has distributed electric propulsion for vertical and horizontal flight. The engines can tilt for vertical takeoff/landing and transition to horizontal flight. The electrical system uses high voltage power supplies to avoid single point failures.

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Optimizing communication in swarms of drones to enable efficient and reliable control of large numbers of drones. The optimization involves clustering the drones into groups with a master drone that communicates with a central server, and slave drones that relay messages from the master. This reduces the number of required communication channels compared to each drone directly connecting. Clustering also allows faster area coverage, obstacle avoidance, and resource sharing. If a master fails, another slave can be promoted. This enables robust swarm operation by minimizing communication breakdowns.

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Identifying the position of a moving object using GPS and notifying of dangerous accidents. The system receives raw GPS position data from the moving object and corrects it using RTCM broadcasts from reference stations. This provides centimeter-level accuracy. The corrected positions are displayed on a map along with other objects, work areas, and danger zones. If distances violate danger thresholds, alerts are shown. This improves safety by providing precise location data and real-time monitoring.

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Magnetic locking system for electric aircraft rotors to prevent unwanted movement during flight. The system uses magnets to lock and unlock the rotors. A magnetic lock with two magnetic components, one fixed on the rotor and one controllable, is engaged using a signal from a controller to prevent rotor movement. This prevents drag from spinning propellers when not in use. The lock is disengaged when needed to allow normal rotor motion.

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Improved control system for drones carrying loads that accounts for the complex motion of the load and drone during flight. The system uses a non-linear filter to fuse sensor data from the drone and load to accurately track the dynamics of the drone-load system. This allows more stable and predictable flight behavior when carrying heavy loads or suspended loads that can swing. The filter model includes parameters like mass, length, inertia, center of mass, and impulse forces from the load and drone thrusters.

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A system for monitoring vertical takeoff and landing sites to identify obstructions that hinder aircraft operations. The system uses cameras to capture images of the site, along with wind direction and speed data. It analyzes the images and wind info to determine the type of obstruction, such as objects or events, that could impede VTOL aircraft takeoff and landing. This helps site managers proactively address identified issues.

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System for mitigating GNSS interference and enabling continuous and accurate GNSS navigation even when GNSS signals are jammed or spoofed. The system uses RF nulling circuits at each antenna to cancel out interference signals. Virtual antenna positions are calculated based on the interference-cancelled RF signals and the physical antenna locations. These virtual positions are used by the navigation system instead of the actual antenna positions. This allows accurate navigation even with GNSS interference since the interference is removed before calculating the virtual positions.

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Enabling high-priority message broadcasts to autonomous devices like drones and self-driving cars in wireless networks like 5G. The broadcasts contain commands for the autonomous devices to execute in response to emergency alerts. The devices receive a notification of an upcoming high-priority message, then the actual message with commands. This allows autonomous devices to respond to emergencies even if no human is present. The messages can also request responses from devices.

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Detecting and responding to flight-restricted regions for unmanned aerial vehicles (UAVs) to permit automated flight control in response to detected proximity to restricted areas. The method involves calculating distances between the UAV and flight-restricted regions using location data. If the distance falls within certain thresholds, flight response measures like landing or preventing takeoff are initiated. This provides automated response to restricted areas with graded actions based on proximity. It also uses location systems to accurately detect restricted areas.

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Sensor fusion for autonomous machines that combines both rule-based and learned processing to achieve higher safety levels and accuracy compared to just using learned processing. The fusion architecture takes advantage of the benefits of both approaches. It retains separate processing streams for each sensor to satisfy safety requirements. These individual outputs are fused using rule-based techniques. Learned processing is used as a hint to improve accuracy. This allows decomposition analysis to decompose sensor contributions. It also enables satisfying higher safety levels by avoiding common cause failures.

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Automating drone waiver requests in emergency situations to expedite approval and reduce delays. The system uses AI to identify and describe incidents, communicate instantly with requesters, and validate data. It aims to provide faster, more efficient waiver processing for emergency drone operations compared to manual methods.

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Preventing camera malfunction caused by high transmission power when wireless communication is active by determining the maximum safe transmission power for the communication module based on the camera. The process involves taking a reference image with transmission off, then a comparison image with high transmission power. Comparing the images reveals if the high power causes camera errors. This determines the safe maximum transmission power for the camera's operation.

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An electronic current control system for autonomous vehicles that automatically limits electrical current to pulsed or switching electronics like motors and LIDAR sensors without latency or user intervention. The system adds a current monitor component to the electrical pathway between the power supply, current control component, and pulsed/switching electronics. The monitor adjusts the current control component based on a threshold voltage compared to the output voltage, without needing the main processor. This provides faster, localized current limiting to prevent unsafe thermal events in the pulsed electronics.

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Braking system for electric vertical takeoff and landing (eVTOL) aircraft that allows flexible takeoff/landing and steering on the ground. The system uses a caliper brake on the wheels that can be controlled by pilot input. The brake resistance is adjustable based on pilot demand to allow steering while rolling. A pilot control device detects steering inputs and generates a brake datum. The controller receives the datum and generates brake commands to resist wheel rotation for steering. This allows the eVTOL to land and takeoff conventionally, as well as steer on the ground using skid-steering.

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Preventing laser beam damage from wireless power systems by detecting and responding to stray beams. The system calculates the difference between transmitted and received power to determine lost beam power. If the lost power exceeds a threshold, the system triggers safety actions like beam modification or shutdown to prevent hazards. The threshold is dynamically adjusted based on the calculated lost power level. This allows accurate and effective safety response over a wide range of lost power values.

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Robot system that can quickly recover from errors during operation by automatically returning the robot to the position where the error occurred and stopping it there. If an abnormality is detected in the sensor input, the robot controller calculates the difference in position between when the error occurred and the current position, and moves the robot back to the error location. This allows easier diagnosis and recovery of errors compared to trying to find the issue in the robot's current position.

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Redundant execution of time-critical control applications in environments without real-time capability. Multiple identical control components execute in parallel to provide redundancy and fault tolerance. They subscribe to periodic data streams and synchronously process the captured data. If a majority agree, they compute outputs. Duplicate outputs are filtered based on sequence numbers. This ensures consistent processing and prevents delayed components from disrupting real-time execution.

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Mobile object control device that improves the accuracy of recognizing nearby objects and generating optimal driving plans when the recognition confidence is low. When confidence is below a threshold, it reduces the image resolution for recognition to capture fewer details, but still identifies nearby objects. If the recognized object's accuracy is still below the threshold, it determines that a larger object with higher risk has been missed. This allows the control system to generate more appropriate driving plans when the confidence of nearby object detection is low.

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Modifying sensor settings of autonomous vehicles based on predicted environmental states to improve performance and safety. The method involves analyzing historical sensor data and environmental conditions to identify patterns and correlations. These patterns are then used to predict future environmental states based on current sensor readings. The sensor settings are then adjusted accordingly to optimize performance in the predicted environment. This adaptive sensor configuration provides better detection and response in changing conditions compared to fixed settings.

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