Low-Signal Navigation for Drones

Localization processing service for autonomous navigation without relying on satellite navigation. The service uses onboard sensors and maps to estimate the position of a vehicle when satellite signals are unavailable. It extracts features from captured images and compares them to map features to find matches and determine location. The service is passive, doesn't require network access, and can work in restricted environments.

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Controlling a vehicle like a drone or robot in an environment using a probabilistic map of the magnetic field. The probabilistic map relates magnetic field measurements to location probabilities. The vehicle measures magnetic field at its current location and submits that along with timestamp to the probabilistic map to estimate location probability. This probability is used in a stochastic control algorithm to safely navigate the vehicle. The probabilistic mapping allows accurate localization despite magnetic field noise and uncertainty.

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Autonomous drone imaging and sensing in datacenters that can continue data collection when network connectivity is lost. The drone uses visual tags placed inside the datacenter to navigate autonomously instead of relying solely on external networks like GPS or Wi-Fi. If connectivity is lost during a mission, the drone switches to autonomous mode using the tags to continue collecting data. This allows uninterrupted drone operations in datacenters with poor network coverage or signal blockage.

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Fully autonomous drone flights where the drone takes off, flies according to a predefined plan, and lands without any further user input beyond an initial command and an endpoint target. The drone uses onboard sensors and mapping to navigate and avoid obstacles. It can also communicate with external systems to gather real-time data for planning purposes. The goal is to enable autonomous drone operations in areas with limited connectivity and for situations where remote control is impractical or unsafe.

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Improving accuracy of mobile delivery devices like drones and autonomous vehicles indoors or in areas with poor GPS signal by switching to an inertial navigation system like gyroscopes when GPS is less accurate. The system senses GPS accuracy indicators and compares them to thresholds. When GPS is determined to be less accurate, it transitions to using the inertial navigation system for positioning. This allows more accurate tracking indoors and in GPS-challenged areas.

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Reliable positioning for drones in areas with poor GPS reception by using a network of ground base stations. When a drone parks, it communicates with nearby base stations to obtain their coordinates. It then estimates the coordinate of a farther base station it can't directly see. This allows the drone to use the nearby base stations as a network to navigate when GPS is unavailable. The parking station stores the base station coordinates for the drone to use during flight.

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Ensuring reliable communication in remote or disaster-affected areas is a technical challenge due to unplanned deployment and mobilization, meaning placement difficulties high operation costs of conventional telecommunications infrastructures. To address this problem, unmanned aerial vehicles (UAVs) have emerged as an excellent alternative provide quick connectivity regions at reasonable cost. However, the limited battery autonomy UAVs restricts their flight service time. This paper proposes system based on tethered UAV (T-UAV) capable continuous through wired power network connected ground station. The communications low-cost devices, such Raspberry Pi platforms, offers wireless IP telephony services, providing high-quality communication. Experimental tests assessed consumption, stability, data transmission performance. Our results prove that T-UAV, quadcopter drone, operates stably 16 V 20 A, ensuring consistent VoIP height 10 m with low latency. These experimental findings underscore potential T-UAVs cost-effective alternatives for extending networks regions, emergency scenarios, ... Read More

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System for geophysical fields sensing-based navigation in environments with intermittent or no data connectivity, where GPS signals may be unavailable. The system uses an offline baseline estimation model stored on the navigation object to estimate geophysical fields like magnetic fields when online model data is not available. It selectively uses the offline model or online data to estimate fields at specified times based on control logic. This allows reliable navigation without constant connectivity.

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Urban Air Mobility (UAM) requires reliable communication and surveillance infrastructures to ensure safe Unmanned Aerial Vehicle (UAV) operations in dense metropolitan environments. However, urban infrastructure is inherently heterogeneous, leading significant spatial variations monitoring performance. This study proposes a unified framework that integrates readiness assessment with Deep Reinforcement Learning (DRL)-based UAV path planning. Using Singapore as representative case, we employ data-driven methodology combining clustering analysis situ measurements estimate the citywide distribution of quality. We then introduce an infrastructure-aware planning algorithm based on Double Q-Network (DQN) convolutional architecture, which enables UAVs learn efficient trajectories while avoiding blind zones. Extensive simulations demonstrate proposed approach significantly improves success rates, reduces traversal through poorly monitored regions, maintains high navigation efficiency. These results highlight potential modeling DRL support performance-aware airspace inform future UAM governanc... Read More

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Abstract Existing collective navigation systems for drone swarms typically rely on the communication between drones, which limits application in specific mission scenarios and reduces robustness against interference. To address this challenge, a communication-free method enhanced by relative state estimation is proposed study. It consists of three key components: visual perception localization, estimation, swarm motion decision. First, sensors are employed to detect nearby drones real time calculate their positions. Second, an optimized model set adaptive interacting multiple (OMSA-IMM) filtering algorithm fuse predicted states from with measurements achieve continuous high-precision positioning. The fused then fed back into decision high-level control. Finally, effectiveness validated through numerical simulations real-world flight experiments. results demonstrate that significantly enhances accuracy localization improves performance algorithm, enabling cohesive collision-free communication-denied environments.

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In Japan, natural disasters occur frequently. Serious may cause damage to traffic networks and telecommunication infrastructures, leading the occurrence of isolated disaster areas. this article, unmanned aerial vehicles (UAVs) are used for data collection instead unavailable ground-based stations in Detailed information about situation will be collected from user equipment (UE) by a UAV through flyhoverfly procedure, then sent response headquarters relief. However, mission completion time minimization becomes crucial task, considering requirement rapid battery constraint UAVs. Therefore, author proposed three-dimensional flight trajectory, discussing optimal altitude placement hovering points transforming original problem K-means clustering into location set cover (LSCP) that can solved via genetic algorithm (GA) approach. The simulation results have shown feasibility method reduce time.

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Positioning information processing method for vehicles that improves accuracy when GPS is unavailable. It combines dead reckoning using inertial sensors with visual odometry using cameras. The method involves obtaining positioning from dead reckoning, then correcting it with visual odometry. Historical dead reckoning data is used to determine distances between past and current positions. These distances are used to correct the current visual odometry. The corrected visual odometry is then used as the final position. This iterative correction and compensation improves positioning accuracy when GPS is unavailable.

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Robotic drone system for autonomous inventory management in indoor environments like warehouses. The drone uses onboard cameras, sensors, and processing to generate a 3D map of the warehouse, estimate its own position and orientation in the map, and autonomously navigate through the map while avoiding obstacles. This allows the drone to operate without GPS and external monitoring in environments where GPS signals are weak or unavailable. The drone can also find optimal paths through the map using its own generated 3D data.

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This article considers a multi-cell wireless network comprising of conventional user equipment (UE), sensor devices and unmanned aerial vehicles (UAVs) or drones. UAVs are used to assist base station, e.g., improve coverage collect data from devices. The problem at hand is optimize the (i) sub-carrier assigned cell (ii) position each UAV, (iii) transmit power following nodes: stations UAVs. We outline two-stage approach maximize fairness-aware sum-rate UE In first stage, genetic algorithm (GA)-based assign sub-band all cells determine location UAV. Then, in second linear program results demonstrate that our proposed achieves approximately 97.43% optimal obtained via brute-force search. It also attains on average 98.78% performance computationally intensive benchmark requires over 478% longer run-time. Furthermore, it outperforms GA-based allocation heuristic by 221.39%.

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Enhanced self-localization for drones using reconfigurable intelligent surfaces (RIS) and fusion algorithm integration to improve accuracy in urban environments where GPS signals are prone to interference. The system fuses RIS-based positioning signals with other sources like GPS, cellular, and IMU to determine drone location using a data fusion algorithm like an extended Kalman filter. Strategically placed RIS antennas provide signals to aid drone navigation along paths and landing at destinations.

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Unmanned aerial vehicle (UAV) with collision avoidance using onboard sensors. The UAV has multiple optical radars to measure distances to obstacles, as well as a visual sensing module for SLAM (simultaneous localization and mapping) to determine obstacle positions. An edge processor converts the SLAM data to match the radar coordinates. The UAV's flight controller uses all the sensor data to maneuver around obstacles. This allows the UAV to effectively avoid collisions without relying solely on GPS or external positioning.

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Abstract To improve the accuracy and efficiency of autonomous navigation for indoor UAVs (Unmanned Aerial Vehicle) in complex substation environments, this paper proposes a multi-sensor fusion-based method. An UAV platform integrating multi-dimensional LiDAR, depth cameras, edge computing devices was designed built. The method uses AprilTag markers as visual references constructs maps based on laser SLAM(Simultaneous Localization And Mapping) technology. A relocation scheme that incorporates information is proposed, Dijkstra DWA (Dynamic Window Approach) algorithms are used to plan global local paths UAV, respectively. In GPS-denied environment, system achieves precise localization environmental point cloud mapping, ensuring accurate positioning path planning. Multi-scenario experiments ROS (Robot Operating System) environment demonstrate provides high strong robustness.

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Structured light navigation aid for autonomous vehicles that uses emitted structured light patterns to improve navigation in GNSS-denied environments. The aid has a movable structured light emitter that projects patterns onto surfaces. A receiver captures reflected light to calculate navigation info. This provides a fixed, drift-free source vs. GNSS-prone drift. The emitter can change direction and pattern. This allows scanning to find suitable landing spots based on features.

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Unmanned aerial vehicle (UAV) localization system that enables precise determination of its absolute position in complex environments through machine learning-based 3D point cloud registration. The system employs a trained model that generates both semantic and depth representations of the environment from camera images, which are then registered against a pre-existing point cloud. This registration process enables accurate determination of the UAV's position and orientation, even in environments with GPS signal loss or sensor malfunctions. The system can be used for autonomous missions where GPS is unavailable or unreliable.

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Optimizing UAV flight paths through AI-driven radio condition adaptation. The method enables UAVs to automatically adjust their flight paths in response to changing radio conditions, leveraging machine learning models to determine optimal path modifications. The approach integrates with UAV networks to dynamically configure network nodes and UAVs based on real-time radio environment conditions, ensuring seamless path adaptation while maintaining network stability.

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Systems and methods for displaying connectivity strength on connected vehicles like drones to help them avoid areas with poor coverage and communication issues. The vehicles receive connectivity data from other vehicles and ground stations, identifying areas with low connectivity. This data is displayed on the vehicle to warn of upcoming poor coverage. The vehicle can also generate updated routes to avoid low connectivity areas. The ground stations transmit specific connectivity data based on vehicle location.

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Abstract: - Conventional tracking systems relying on GPS and cellular infrastructure are ineffective in environments with little or no connectivitysuch as remote wilderness, mountainous regions, disaster-affected areas. This paper introduces a hybrid architecture that integrates satellite communication, mesh networking, Radio Tomographic Imaging (RTI), signal jumping, drone-assisted relays, Low-Power Wide-Area Networks (LPWAN). The proposed system addresses loss challenges by utilizing AI-driven prediction autonomous drones to extend coverage improve real-time traceability. Performance is evaluated through simulations real-world case studies based key metrics including coverage, latency, energy efficiency, reliability. approach demonstrates strong potential for critical applications search rescue, defense operations, systems, contributing toward the development of resilient off-grid communication technologies. Keywords: Remote Tracking, Dead Zones, Mesh Networks, Satellite Communication, Drone Relays, RTI, LPWAN, Signal Prediction, Off-Grid Search Rescue.

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Enabling unmanned aerial vehicles (UAVs) to navigate autonomously without relying on cameras and compasses when environmental conditions like low light or magnetic interference impair their effectiveness. The UAV uses a global navigation satellite system (GNSS) to determine its position and velocity in a world frame of reference. It also measures acceleration and angular rate signals from onboard accelerometers and gyroscopes to determine acceleration and orientation in a navigation frame of reference. By combining GNSS positioning with inertial measurements, the UAV can navigate autonomously even when cameras and compasses are unreliable.

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Navigation system for unmanned aerial vehicles (UAVs) that can autonomously navigate and locate themselves without GPS in GPS-denied areas. The system uses onboard cameras to capture images of the terrain below the UAV. Object detection using machine learning identifies objects in the images. Comparing these detected objects with objects in a preloaded map allows locating the UAV based on matched objects. With the UAV location, it can navigate to targets using the map. This enables UAVs to continue autonomous flight in GPS-denied areas.

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The Flying Ad-Hoc Network (FANET) is an important component of the 6G communication system. In order to achieve precise positioning unmanned aerial vehicle (UAV) nodes in a FANET when satellite navigation signals are unavailable, simple and accurate direction-of-arrival (DOA) estimation methods required. this paper, we propose improved correlative interferometer method estimate DOAs UAVs FANET. This adopts uniform funnel array (UFA) configuration, which consists circular (UCA) additional element located above center. configuration improves accuracy for with large polar angles because it utilizes degree freedom vertical aperture. addition, Fibonacci sampling strategy employed overcome clustering phenomenon exhibited by latitudelongitude sampling. Furthermore, interferometer, only partial phase differences used reduce storage burden. When calculating similarity function, adopt triangular function instead cosine improve computational efficiency. simulation results show that proposed UFA DOA 65.56% over planar UCA angles. Moreover, 11.54% as compared

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Method to improve GNSS receiver sensitivity by extending coherent integration time while mitigating the negative effects of dynamic motion. It compensates for receiver acceleration during long coherent integration by using Doppler estimates from inertial measurements instead of relying solely on the PLL/DLL. This allows increasing integration time even if the signal is too weak to lock. At each epoch, the carrier phase is initialized based on a previous Doppler estimate, then updated using current Doppler estimates. This accounts for receiver motion during integration.

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Assisted wireless device positioning using a vehicle in a wireless network to improve accuracy compared to relying solely on cell towers. The vehicle can be an unmanned aerial, ground, or responder vehicle. It associates with the target device's network, triggers positioning using signals between the vehicle, device, and fixed access points, and determines the device's position. This leverages the vehicle's proximity and potentially better radio conditions compared to the target device. It allows more accurate positioning when the target is indoors or has poor signal.

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Determining the flight path of an air vehicle like a drone using the location of a nearby device as a reference instead of absolute coordinates. The method involves acquiring the position of a ground device like a phone or sensor at a known location using corrections based on nearby reference stations. The air vehicle's flight path is then determined based on the ground device's location rather than absolute coordinates, providing more accurate and reliable path planning.

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A system and method for navigating fixed-wing unmanned aerial systems (UAS) in environments without or with degraded global positioning System (GPS) signals. The method uses relative motion estimation and optimization to improve local navigation and leverage occasional GPS measurements and cooperative constraints from other UAS for global positioning. The UAS estimates its motion relative to the environment using an onboard sensor fusion algorithm like an extended Kalman filter. It then optimizes a back-end pose graph representing global position by incorporating local motion estimates and occasional GPS measurements as constraints. Sharing range measurements and resetting simultaneously between UAS allows leveraging cooperative constraints. This improves accuracy compared to relying solely on local sensing in GPS-denied environments.

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A moving body like a drone that can switch between air, water, and land environments and accurately estimate distances between itself and other communication devices in each medium. The drone estimates distances by selecting an appropriate profile based on the current medium and the transmission path characteristics between the drone and the other device. This compensates for the different signal attenuation and propagation characteristics in air, water, and land.

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Aircraft guidance in areas with weak or compromised GPS signals using a network of mobile platforms. The aircraft calculates its position relative to a nearby mobile platform based on signals between them. It then uses the relative position and the mobile platform's known position to calculate the aircraft's absolute position. This allows accurate navigation in contested areas without relying solely on GPS.

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Navigation system that provides reliable position estimates even when external signals are unreliable. The system uses parallel calculation units with different sets of input sources. One unit uses only inertial and terrain data, while the other adds external signals. They compare reliability and select the better estimate. This allows identifying problematic sources and continuing with a more trustworthy estimate when external signals fail.

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Moving body like a drone that improves alignment accuracy during autonomous movement by determining optimal directions based on network topology and transmission characteristics. The moving body wirelessly communicates with external devices to acquire network connection relationships and transmission path characteristics. It then uses this information to determine directions for movement, rather than relying solely on sensors or GPS. This improves alignment accuracy, especially in environments where visibility or accuracy is poor.

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Magnetic navigation and localization system that uses onboard magnetometers instead of GPS to provide more reliable positioning in areas where GPS is unreliable. The system leverages the stable and predictable magnetic field of the Earth to provide location information. It measures the magnetic field using magnetometers and processes the data to determine position. This allows accurate and consistent location data in areas like buildings, tunnels, or urban canyons where GPS signals are weak. The system compensates for magnetic anomalies and drifts using known maps and models of the magnetic field.

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Aiding data processing to increase reliability and availability of positioning, navigation, and timing (PNT) information using signals of opportunity (SoOPs) that are not primarily intended for PNT. The technique involves supplementing SoOP signals with additional information called aiding data to compensate for the fact that SoOPs are not optimized for PNT. The aiding data can be derived from sources other than the SoOPs themselves. By processing the SoOP signals along with the aiding data, it improves the accuracy and reliability of PNT estimates when traditional PNT sources like GPS are unavailable or degraded.

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Route navigation system that switches to SLAM-based positioning when GPS is unreliable. It uses SLAM (simultaneous localization and mapping) to navigate when GPS positioning becomes inaccurate. The system initially uses GPS for augmented reality navigation. When GPS positioning accuracy degrades, it converts SLAM coordinates into calculated geographic coordinates and continues augmented reality navigation using the SLAM-based coordinates. This avoids wrong route indications and improves accuracy when virtual and real scenes are combined.

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Navigation receiver that can estimate position, velocity, and time using fewer than four transmitters, like GPS satellites. The receiver can calculate position with just three transmitters if it knows the receiver's velocity. With two transmitters and known clock drift, it can estimate position. This enables positioning when some transmitters are missing or obstructed, or for hybrid systems with fewer satellites. The technique combines ranging and Doppler equations to solve for position with fewer transmitters.

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Abstract Navigation without Global Satellite Systems (GNSS) poses a significant challenge in aerospace engineering, particularly the environments where satellite signals are obstructed or unavailable. This paper offers an in-depth review of various methods, sensors, and algorithms for Unmanned Aerial Vehicle (UAV) localization outdoor GNSS unavailable denied. A key contribution this study is establishment critical classification system that divides GNSS-denied navigation techniques into two primary categories: absolute relative localization. enhances understanding strengths weaknesses different strategies operational contexts. Vision-based identified as most effective approach environments. Nonetheless, its clear no single-sensor-based algorithm can fulfill all needs comprehensive Therefore, vital to implement hybrid strategy merges sensors outcomes. detailed analysis emphasizes challenges possible solutions achieving reliable UAV unreliable multi-faceted analysis, highlights complexities potential pathways efficient dependable

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Real-time urban location determination using images when GPS is unavailable or inaccurate. The method involves extracting background features from a captured image and matching them against a library of reference background images with known locations. By finding the best match, the location of the captured image can be determined. The background feature extraction uses convolutional neural networks. This allows accurate urban location determination even when GPS signals are poor or blocked.

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Path setting method for an unmanned aerial vehicle (UAV) that allows it to continue flying without interrupting GPS signal even when it loses line of sight with satellites. The method involves calculating the number of visible GPS satellites based on terrain elevation data, comparing it to a minimum required number for flight, and adjusting the UAV's vertical and horizontal positions if necessary to maintain enough satellite visibility. This prevents GPS signal loss and allows safe flight even over obstructed areas.

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Determining location using multiple sources with decreasing trust levels, like celestial navigation, inertial navigation, and GPS. If the highest trust source (celestial) is available, use it. If not, fall back to lower trust sources (INU, GPS). This provides reliable location without relying solely on satellites or GPS.

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Navigation system for autonomous vehicles that allows them to accurately locate themselves within a working area without external infrastructure like GPS or visual markers. The system uses a transmitter with an emitter, power source, wireless charging receiver, and printed circuit board. The autonomous vehicle has a receiver to detect the signals emitted by the transmitter. A processor determines the vehicle's relative location within the working area based on the received signals. The wireless charging allows the transmitter to operate autonomously without external power sources. Multiple transmitters with unique signals can provide redundancy and improve location accuracy.

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Geomagnetic navigation technique using artificial intelligence to enhance geomagnetic mapping resolution and improve vehicle navigation accuracy when traditional satellite-based systems are compromised. The technique involves generating higher resolution synthetic geomagnetic maps using a generative adversarial neural network trained on lower resolution maps. These enhanced maps are then used along with other sensors for vehicle navigation. The AI also learns adaptive covariance adjustments for particle filtering to further enhance state estimation accuracy.

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A detachable drone system that allows a primary drone to release a secondary drone mid-flight to improve signal strength and overcome obstructions. The primary drone has a camera and displays video to a user's headpiece. When signal quality drops, the primary drone launches the secondary drone to fly alongside it. The secondary drone has its own sensors and can detach to continue signal relay. They fly in tandem to maintain signal strength until the secondary drone is no longer needed. The detachable relay drone improves video quality and reliability by extending range and avoiding obstructions.

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Drone system with detachable relay drone for improved signal quality and object tracking. The system has a primary drone with a camera and detachable secondary drone. If signal quality drops below a threshold, the primary drone releases the secondary drone to fly alongside and improve signal. The drones coordinate flight to maintain best signal until the secondary reattaches. This allows stable video transmission from remote areas without obstructions.

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Verifying positional information for navigation systems in GPS-denied environments to improve reliability. It involves continuously monitoring sensor data from multiple onboard sources and calculating trustworthiness metrics for each sensor. The sensor with the highest trustworthiness is used to update the inertial navigation units. This allows the system to automatically select the most reliable sensor when GPS is unavailable or spoofed. The trustworthiness metrics are calculated by comparing sensor readings against each other to determine deviations.

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Enhancing positioning accuracy, reliability, and adaptability of drones used for power grid inspections by combining Beidou satellite navigation with inertial navigation. The method involves fusing data from both systems using an extended Kalman filter to improve positioning accuracy compared to using just Beidou. The filter adapts weighting based on factors like BDS signal quality, INS performance, and environment. This allows reliable positioning even in areas with satellite blockage or when INS errors accumulate. The combined navigation also enables better dynamic response and environmental adaptability compared to relying solely on Beidou.

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High-precision positioning method for unmanned aerial vehicles (UAVs) operating in complex power transmission environments using sensor fusion and multi-sensor data processing. The method involves integrating visual, inertial, and GPS sensors to provide accurate positioning and navigation for UAVs in power transmission line inspection applications. It combines visual odometry from cameras with inertial navigation and GPS to mitigate errors and improve precision. The sensor data is optimized using a factor graph algorithm and sliding window optimizer to fuse and correct the measurements. This provides robust and accurate positioning for UAVs in challenging power line environments with complex geometry and obstructions that can degrade GPS and visual sensing.

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Robust autonomous positioning and mapping for drones in GPS-denied environments using a fusion of UWB and IMU sensors. The method improves drone positioning accuracy indoors and in other GPS-challenged areas by combining UWB range measurements with IMU inertial data. The UWB provides centimeter-level positioning but is prone to interference and terrain effects. The IMU provides high-frequency attitude data to compensate. The sensors are fused using an unscented Kalman filter to estimate pose and map indoors without external GPS.

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Wireless networking positioning system for small drones that enables indoor navigation and positioning using a combination of WIFI, GNSS, UWB, cameras, and barometers. The system allows drones to transition between outdoor GPS and indoor positioning using WIFI-based localization when GPS is unavailable. The drone has two WIFI modules, one for data communication and another for positioning. It also has a GNSS receiver, UWB chip, camera, barometer, and interfaces for cameras, flight controls, power, and barometer.

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