International Drone Regulations

Automated approval system for drone flight applications that uses AI and machine learning to quickly and efficiently approve drone flight applications. The system analyzes the application data to determine if it meets regulatory requirements, reducing the need for manual review. This improves approval times and enables faster, safer drone integration into airspace.

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A method and system for assessing collision risks between unmanned aerial vehicles (UAVs) before flight. The system processes flight data from multiple UAVs to calculate collision probabilities between them. It involves identifying key points on the UAV trajectories that represent potential conflicts, and calculating risk metrics based on factors like proximity, relative speed, and flight direction at those points. The system can provide pre-flight collision risk assessments to help UAV operators mitigate conflicts and ensure safe operations.

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Method to improve accuracy of unmanned aerial vehicle (UAV) flight requests by verifying legality based on UAV and pilot identities, and adjusting flight plans if rejected. The method involves receiving a UAV flight mission, verifying legality using identity checks, planning the flight path, submitting the request, and modifying the path if rejected based on reasons. The identity checks include matching UAV and pilot info against a database, calculating hash values, and comparing them.

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Low-altitude drone detection and early warning system for airspace management that uses joint detection, risk assessment, and situation sharing to improve safety and compliance of drones operating in low-altitude airspace. The system involves predefining sensitive airspace areas, jointly detecting drones using active and passive sources, assessing risk based on factors like weight and proximity, and sharing alerts to surrounding aircraft when unauthorized drones are found. This allows accurate identification, risk evaluation, and timely warning of illegally flying drones to prevent conflicts and ensure continued safety and stability of low-altitude airspace.

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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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An air traffic control system for managing unmanned aerial vehicles (UAVs) uses existing wireless networks like cell networks to enable safe drone delivery operations. The system monitors and controls UAV flights and 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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Wirelessly detecting, identifying, locating, and neutralizing unauthorized unmanned aerial vehicles (UAVs) within a predetermined area. It uses a distributed network of radio nodes that passively fingerprint the UAV's RF signals to identify it. By detecting the signals at multiple nodes, they can triangulate the UAV's location. The network can then generate and transmit control signals on the same frequency to take over control of the UAV.

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Manage and restrict drone operations and equipment to prevent unauthorized use and avoid bad actors gaining access to restricted capabilities. It involves an off-board computer that checks user licenses and selectively provides equipment control software to the drone based on the license. If a license authorizes certain equipment, it will transmit the proper software to operate it. This ensures the drone cannot operate unauthorized equipment.

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Aerial unmanned vehicle privacy breach defense infrastructure that can detect drones within a threshold distance of an individual, alert them, track the drones, and execute countermeasures. The system includes a mobile app with detection, alert, tracking, and countermeasure modules. It uses sensors like microphones, cameras, radar, or radio frequency detectors to identify nearby drones. Countermeasures can involve disabling drones with electromagnetic signals or lasers or deploying other drones for interception.

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Controlling aerial vehicle communication to optimize data transfer and reliability between aerial vehicles. The control involves dynamically setting the operating mode of each aerial vehicle based on flight plan and network quality information. The operating modes are: 1) sending data between vehicles through the communication network and 2) sending data directly between vehicles without using the network.

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Efficiently intercepting unmanned aerial vehicles (UAVs) that enter restricted airspace without the expense and complexity of fighter jets or high-powered countermeasures like nets, lasers, or jammers. The interception method involves launching a cloud of small objects into the projected path of the target UAV to disable it. The objects ensnare propellers or block airflow to disrupt lift and cause an automatic landing. Probability analysis of the UAV's projected path determines when and where to launch the intercepting objects.

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System and methods for managing unauthorized airborne drones communicating with cellular networks. The system detects unauthorized drone operations and restricts its network access, preventing wasted resources. The network node can also send a message indicating the connection will be released, with conditions for reconnection, or perform a forced detach procedure.

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Detecting connectivity anomalies in a flight area to ensure the 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 so that UAV guidance can be adjusted.

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Geofence management for unmanned aerial vehicles (UAVs) to enhance safety and regulation. The system uses sensor data from in-flight UAVs to detect objects and create geofences around them. This allows UAVs to detect and avoid objects not visible to their own sensors. The geofence management uses data from multiple UAVs to improve accuracy and reliability. It can create geofences around objects detected by one UAV that other UAVs can then avoid.

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An automated system helps unmanned aerial vehicle (UAV) pilots plan and monitor flights to avoid airspace restrictions. The system uses a checklist of flight restriction conditions generated based on a planned flight area and time period. The system also monitors for changes to the restrictions and notifies the pilot before the flight if any restrictions have changed.

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Intelligent approval method for unmanned aerial vehicle (UAV) airspace use that automates and improves the efficiency and accuracy of UAV airspace approval decisions. The method uses an expert system with rules based on approval experience. The rules cover data integrity, airspace restrictions, and safety impacts. The rules are prioritized based on planning information. The system sequentially applies rules to review UAV plans. It provides an automated approval process that leverages expert knowledge to make more accurate and consistent UAV airspace decisions.

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Monitoring and regulating unmanned aerial vehicles (UAVs) to improve safety and security without requiring physical modification to the UAV. The method involves detecting UAVs by capturing and analyzing datagrams transmitted by the UAVs. The datagrams contain monitoring data like UAV and controller locations. The UAVs are configured to transmit these datagrams along with their normal communications. This allows existing UAV communication systems to be used for monitoring.

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A device and method to automatically track and disable unauthorized drones using multiple cameras and a pan-tilt electromagnetic module. Cameras detect and track the drone's movements. A control unit analyzes the video and moves the pan-tilt module to keep the drone in view. It can then aim an electromagnetic pulse to disable the drone's systems. The automated tracking and aiming improve response time and accuracy compared to manual anti-drone systems.

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Risk management system for unmanned aerial vehicle (UAV) inspection operations to improve safety and efficiency. The system has three modules: airspace approval, flight planning, and comprehensive management. The airspace approval module reviews UAV inspection plans to assess risks. Flight planning generates detailed UAV routes based on inspection plans. Comprehensive management monitors real-time UAV flights against planned routes to detect deviations and abnormalities.

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Full-process supervision of unmanned aerial vehicles (UAVs) using blockchain to enable efficient, coordinated, and transparent supervision from UAV production to operation. The system involves using a consortium blockchain to track and verify UAV lifecycle events like manufacturing, registration, flight authorization, and flight data. It enables real-time monitoring, data sharing, and traceability between regulatory agencies, UAV makers, and operators. The blockchain provides a secure, tamper-proof ledger to ensure accurate, auditable supervision across departmental boundaries.

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