Protective Controls in Drone Operation

Dynamic collision avoidance for multiple drones operating in a shared airspace. The system employs a multi-stage approach to prevent collisions between drones, where each drone continuously monitors its surroundings and adjusts its behavior based on the presence of other drones. The system uses a combination of real-time imaging, collision detection, and path management to dynamically reorient and change drone paths to avoid collisions. This approach enables efficient collision avoidance while maintaining operational flexibility and adaptability among multiple drones.

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Mitigating potential traffic encounters between an unmanned aerial vehicle (UAV) and manned aircraft operating in the same airspace. The UAV plans its flight path by considering the predicted trajectories and uncertainty volumes of other aircraft in the airspace. It receives data on current positions and parameters of manned aircraft, determines initial uncertainty volumes around them, predicts future trajectories, and compiles a composite uncertainty space. The UAV then plans its path to avoid intersecting this space.

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Firefighting operations under risky conditions expose crews to risks such as heat and poor visibility. This project is focused on developing manufacturing a micro drone enhance rescue effectiveness by navigating congested spaces making real-time observation. The development process consists of aerodynamic modelling in SolidWorks ensure stability maneuverability, functionality enhanced using high-performance materials, sensors, optical systems. Accuracy achieved vertical tower milling produce accurate parts. flight time obstacle avoidance are tested the prototype.

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Automated flight response for unmanned aerial vehicles (UAVs) to comply with airspace restrictions around airports and other no-fly zones. The UAV calculates its distance from restricted areas using its own location and the location of the restrictions. If the distance is below a threshold, it takes immediate action like landing or preventing takeoff. If the distance is greater, it allows normal flight. This allows automated compliance with airspace rules while providing flexibility for safe operations when farther away.

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Obstacle avoidance for drones that balances proactive and reactive collision avoidance while tracking targets. The drone dynamically adjusts its flight characteristics based on obstacle proximity. If an obstacle is far away, it proactively moves to maintain a safe distance. If an obstacle is close, it reacts quickly to avoid collision. The drone evaluates candidate motion adjustments and selects the best one based on route optimization. This allows it to proactively steer around obstacles that may cause immediate threats versus reactively evading imminent collisions.

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Autonomous landing of drones in complex environments using adaptive path planning and deep reinforcement learning. The method selects between local and global path optimization based on perception range. For local optimization, drones plan paths around nearby obstacles. For global optimization, they use perceived frontiers. This improves efficiency by avoiding redundant planning. For landing, a neural network learns to control the drone using reward functions. This increases neural network update efficiency compared to traditional methods.

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This article studies an anti-collision control method for the trajectory tracking problem of unmanned aerial vehicle aircraft skin inspection under complex wind disturbance. To guarantee safety during inspection, disturbance is described by maximum position offset constraint vehicles (UAVs). Then, exponential nonlinear integral super-twisting sliding mode (ENISTSM) controller designed, and incorporated with prescribed performance control(PPC) to ensure that error consistently constrained within specified bounds. Subsequently, attitude angular velocity cascaded law investigated based on modified Rodrigues parameters (MRPs) representation using (ESTSM) method. The proposed achieves fast relatively large variable angles robust In addition, stability controllers proven via Lyapunov analysis. Finally, simulation results are included considering turbulent field demonstrate effectiveness advantages.

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Safe landing point search method for unmanned aerial vehicles (UAVs) on unfamiliar terrain using terrain maps and contour lines. The method involves generating contour lines based on a minimum height and interval from a terrain map. Then, it searches for largest empty circles (LECs) with a minimum radius in the contour map. The UAV is provided the LEC with the largest radius as a safe landing spot. This leverages contour lines to find flattest areas without obstructions for UAV landing.

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Method and system for generating optimal flight paths for drones operating beyond visual line of sight (BVLOS) to reduce collision risk. The method involves calculating out-of-LOS times for potential flight paths, determining additional observation times when substitute observers can see the drone, and generating scores based on comparisons and additional factors. The recommended path is the one with highest score.

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UAV collision avoidance method that allows multiple UAVs to fly safely in proximity without constant communication. UAVs exchange flight information with nearby UAVs. They calculate intersection times based on the received flight info. If an intersection is imminent, they adjust their flight paths to avoid collision. This allows UAVs to dynamically adapt their paths to prevent mid-air collisions without constant communication.

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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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