UAV Routing Maps with Visual Tracking for GNSS Failure
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) relying primarily on Global Navigation Satellite System (GNSS) navigation face failures due to erroneous or weak signals, leading to navigation inaccuracies, especially in feature-poor areas like large bodies of water, deserts, or forests, rendering them inoperable.
Innovation Solution
Implementing a camera-based navigation system as a secondary navigation method that generates and uses three-dimensional maps with tracking parameters to determine and maintain the UAV's position, allowing it to plan and follow routes even when GNSS is unreliable, by capturing images and processing them to identify distinctive features and assign tracking parameters to sections of the mapped area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS-based navigation system is used for UAV positioning, then navigation accuracy is improved in most areas, but the system becomes unreliable in feature-poor areas such as large bodies of water, deserts, or forests
Solution Approach 1:
The patent combines GNSS-based navigation with camera-based navigation systems to create a hybrid positioning system. The camera captures images of the ground and uses feature detection algorithms to identify distinctive features and calculate position, providing reliable navigation in areas where GNSS signals are weak or erroneous, thus resolving the reliability issue in feature-poor areas while maintaining the accuracy benefits of GNSS in suitable environments
Solution Approach 2:
The patent introduces camera-based feature detection as an intermediary system that bridges the gap when GNSS fails. The camera system detects ground features and uses computer vision algorithms to determine position, acting as a mediator that provides continuous navigation capability in environments where direct GNSS reception is unreliable
2Reliability
If camera-based navigation system is implemented as backup, then reliability during GNSS failure is improved, but device complexity increases
Solution Approach 1:
The camera system serves multiple functions: it captures navigation images for position determination, provides visual recording of the flight path, and can detect ground features for obstacle avoidance. This multi-functionality justifies the added complexity by providing reliable backup navigation while reducing the need for separate dedicated systems
Solution Approach 2:
The navigation system uses the camera's inherent imaging capability to serve its own navigation needs without requiring additional specialized sensors. The same camera that records visual data can be processed through computer vision algorithms to extract position information, allowing the system to self-service its navigation requirements and reducing overall system complexity
Data Source
AI summary
An unmanned aerial vehicle (UAV) includes a propulsion system, a global navigation satellite system (GNSS) sensor, a camera and a controller. The controller includes logic that, in response to execution by the controller, causes the UAV to in response to detecting a loss of tracking by the GNSS sensor determine an estimated location of the UAV on a map based on a location image captured by the camera, determine a route to a destination using tracking parameters embedded in the map, wherein the map is divided into a plurality of sections and the tracking parameters indicate an ease of determining a location of the UAV using images captured by the camera with respect to each section, and control the propulsion system to cause the UAV to follow the route to the destination.