UAV Visual Landmark Navigation in GPS-Denied Flight Paths
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Solution Overview
Problem
Existing unmanned aerial vehicle (UAV) systems face challenges in navigating through GPS-denied areas due to interference, spoofing, or unavailability of satellite signals.
Innovation Solution
A navigation system is implemented on the UAV, utilizing a camera to record images of terrain, which are then processed by a machine learning model to detect objects. These detected objects are compared with an electronic map loaded on the UAV, allowing the system to determine the UAV's location and navigate to a target location without relying on GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS navigation is used for UAV navigation, then navigation accuracy and availability are improved, but the system becomes vulnerable to signal interference, spoofing, and denial in certain areas
Solution Approach 1:
The patent introduces visual landmarks and machine learning-based object detection as an intermediary navigation mechanism. Instead of directly relying on GPS signals, the system uses detected objects (buildings, towers, distinctive structures) as mediators to determine position and navigate, thereby eliminating direct dependence on vulnerable GPS signals while maintaining navigation functionality in denied areas
Solution Approach 2:
The patent replaces the electromagnetic-based GPS navigation system with a visual-based navigation system using cameras and machine learning models. This substitution involves capturing images, detecting objects through ML algorithms, matching them with pre-stored landmark data, and calculating position accordingly, thus replacing signal-based navigation with image-processing-based navigation
2Reliability
If visual-based navigation using machine learning is implemented, then navigation reliability in GPS-denied areas is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-storing visual data of known landmarks and their corresponding GPS coordinates before the navigation mission. The system pre-processes and stores image data, object characteristics, and location information, so that during actual navigation in GPS-denied areas, the system can quickly match detected objects with pre-stored data without complex real-time computations, thereby reducing operational complexity
Solution Approach 2:
The patent segments the navigation system into distinct functional modules: camera subsystem for image capture, machine learning-based object detection subsystem for identifying landmarks, object matching subsystem for comparing detected objects with pre-stored data, and position calculation subsystem for determining location. This segmentation allows each module to be optimized independently and simplifies the overall system architecture and maintenance
Data Source
AI summary
Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.