Advanced Drone Navigation Techniques in Poor Signal Areas

Drone positioning and navigation that doesn't rely on GPS satellites, which can be unreliable. The system uses ground base stations located in the drone's flight field. The drone communicates with nearby base stations to obtain positioning signals. If GPS is lost, the drone can still navigate by triangulating signals from the base stations. This provides a backup positioning method that works in environments with poor GPS reception.

Navigation system for unmanned aerial vehicles (UAVs) that does not rely on GPS and can work in urban environments with limited satellite coverage. The system uses two synchronized periodic wideband signals transmitted from base stations. The UAV receives the signals and determines its position relative to the base stations based on the signal reception times and intensities. This allows the UAV to navigate along a flight path defined by the base stations without GPS.

Deploying a visual navigation system for UAVs that provides reliable positioning when GPS signals are unavailable or unreliable. The technique involves placing multiple geo-fiducials around a landing pad, each with a unique offset and direction from a surveyed center point. The UAV uses computer vision to recognize and triangulate the geo-fiducials for precise navigation.

Supporting unmanned aircraft to navigate and land safely if communication with the ground station is lost. When the aircraft loses its data link, it conveys its intent to navigate and land via voice communications on the radio frequency assigned for the airspace. The aircraft composes a message indicating its intent to execute a preconfigured lost-link procedure, converts it to speech using text-to-speech, and sends the voice message over the radio channel.

Monitoring GNSS coverage and predicting availability to enable unmanned aircraft to operate safely without backup navigation systems by analyzing GNSS data, almanacs, and network structures to detect and notify about potential GNSS signal loss along planned routes.

Planning UAV routes through areas with varying wireless signal quality to ensure reliable communications. The method involves creating a dataset of signal quality at multiple locations in the area. This data is then used to plan a route that minimizes or avoids areas with poor signal quality. The goal is to optimize the route to maintain target signal quality for the duration of the flight.

Calibrating sensors on a UAV while in flight to ensure data accuracy when the UAV goes out of communication range. The method involves detecting changes in spatial sensor configurations and adjusting the data based on the new configurations. This allows the UAV to dynamically recalibrate onboard sensors without remote access when calibration drifts.

Tracking unmanned aerial vehicles (UAVs) without reliance on GPS. The tracking uses optical tracking and image recognition. A ground camera tracks a retroreflector on the UAV through optical tracking. Image recognition is used as a backup when optical tracking fails. The ground station can then guide the UAV based on the tracking data.

A device for locating a target such as a drone on-board camera that can locate a target using only an inertial unit and camera. The device does not need an external reference like GPS or stellar correction. It uses an inertial unit to provide position and orientation data of the camera, and a camera to acquire images of the target. The acquired images are used to calculate the position of the target relative to the carrier.

Enabling accurate autonomous package delivery to locations with unclear or low confidence GPS positions by leveraging learning from transport devices to map pickup and drop-off zones. The system uses video recording, AI learning, custom POIs, and characteristics of areas (like trees) to identify and validate delivery points. It also allows marking multiple delivery areas per property.

Autonomous vehicle operation in environments where GPS and communication signals are degraded or unavailable. The method involves using onboard sensors and pre-loaded 3D area maps to estimate the vehicle's location without GPS. Objects detected in the environment are matched to the maps to obtain their known locations, providing a reference for position estimation. Image recognition, distance sensors, and flight profile integration are used to enhance accuracy.

Surveying device for photogrammetry using UAVs without requiring GPS or ground control points to improve the accuracy of photogrammetry. The device analyzes UAV camera photographing conditions and timing to associate them with surveying device position data. This allows accurate positioning of the UAV camera images for photogrammetry even without GPS or ground control points.

A method for unmanned aerial vehicles (UAVs) to determine their location without relying on GPS or other external systems. The method involves UAVs capturing images of known ground structures during flight, extracting the location of those structures, and then sharing that temporal and spatial information with other nearby UAVs. By triangulating the time of flight of the shared information, UAVs can determine their own locations using the known ground structures as reference points.

System for collecting flight information from drones in areas where traditional communication networks are unavailable. The system uses a relay aircraft flying between the drone and a satellite to forward the drone's information to a ground station via satellite communications. The relay aircraft receives the drone's data and sends it to the ground via the satellite, allowing drone flight information to be collected even in areas without mobile network coverage.

An aircraft control device and a remote control aircraft can prevent loss of control when the transmission channel between the remote and the aircraft fails. The aircraft has multiple receivers that can receive control information from different sources. This allows control information to be received from the primary remote control as well as secondary sources like a backup remote or ground control station. If the primary remote fails, the aircraft can still receive control signals from the backup sources and maintain flight control. This redundancy prevents loss of control if the primary control channel is disrupted or out of range.

Guiding unmanned aircraft when GPS is unavailable using onboard sensors like cameras instead. The guidance system tracks a target in the camera image while commanding the aircraft to match its optic flow to that of the target. This optic flow difference between the moving aircraft and stationary target provides guidance information that can be used to navigate the aircraft in the absence of GPS.

Improved remote control of unmanned helicopters that allows accurate flight path repetition without reliance on GPS. The unmanned helicopter flight is controlled based on relative position from an updatable base point rather than absolute GPS coordinates. The remote control device has sensors to detect flight orientation, speed, and distance. It calculates the relative position from the base point using integrated speed data. This allows repeating accurate flight paths even if GPS is unavailable.

Flight control system for unmanned aerial vehicles that can accurately control position and velocity without relying on GPS signals. It uses visual scene analysis to determine the UAV's location and then switches between height measurements from cameras or ultrasound sensors depending on scene textures.

Determining the in-air position of an aircraft, such as a drone, without using GPS or barometers. The techniques involve capturing images of the ground during pre-flight calibration and then during flight using a downward-facing camera, and comparing those images to determine the aircraft's position. The images are compared to a database of known ground images to find matches, and the distances between matched features are used to calculate the aircraft's position.

A marker system for guiding drones to delivery locations with high accuracy and reducing the risk of collisions. The marker uses a set of lights that can be individually sequenced to create readable light patterns like QR codes. The lights can convey information to drones to guide them to the correct delivery location without relying on GPS alone. The marker can also detect obstacles like people and animals and display a warning light pattern to tell the drone to avoid landing.