UAV GPR Mapping Buried Objects
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Solution Overview
Problem
Current Ground Penetrating Radar (GPR) sensor equipment requires intimate contact with the ground surface to effectively identify buried objects in subterranean surfaces, losing useful data when lifted even slightly.
Innovation Solution
An unmanned aerial vehicle (UAV) system integrated with radar sensor equipment, including a Global Navigation Satellite System (GNSS), Synthetic Aperture Radar (SAR), and Ground Penetrating Radar (GPR) sensors, capable of generating raw images and identifying objects buried underground from heights of up to 50 feet, using machine learning algorithms and calibration data for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPR sensor equipment is placed in intimate contact with the ground surface, then detection accuracy is improved, but mobility and accessibility are reduced
Solution Approach 1:
The patent transitions GPR sensing from ground-level contact to aerial operation by mounting sensors on a drone platform. This dimensional shift from 2D ground surface to 3D aerial space enables the system to maintain detection capabilities while gaining mobility and access to previously unreachable areas.
Solution Approach 2:
The patent introduces an aerial platform (drone) as an intermediary carrier between the GPR sensor and the ground target. This intermediary enables the sensor to operate at elevated positions while still effectively detecting subsurface objects, resolving the contradiction between contact requirement and mobility.
2Ease of operation
If GPR sensor equipment is lifted off the ground surface, then mobility is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent modifies operational parameters including flight altitude, sensor orientation, and signal processing techniques to optimize GPR performance from an aerial platform. By adjusting these parameters, the system maintains detection accuracy despite being elevated off the ground surface.
Solution Approach 2:
The patent implements dynamic control of the drone's flight characteristics and sensor positioning to adapt to different detection scenarios. This dynamic adjustment enables the system to optimize detection accuracy for various object depths, soil conditions, and flight scenarios.
3Adaptability or versatility
If multiple sensor types are integrated on the UAV, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent integrates multiple sensor types (GPR, optical, inertial) on a single UAV platform to create a multi-functional detection system. This universal platform can perform various detection tasks including subsurface imaging, surface inspection, and navigation, reducing the need for multiple separate systems.
Solution Approach 2:
The patent combines multiple sensor systems and data processing functions into an integrated UAV platform. By merging these components and implementing coordinated data processing, the system achieves enhanced detection capability while managing complexity through integration rather than separate operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables non-contact mapping of manmade objects and structures beneath various soil compositions, improving detection depth and accuracy, and allowing aerial access to areas inaccessible by ground-based equipment, with the ability to detect objects as deep as 120 meters under different soil conditions.
Implementation Method 1
a GPR sensor disposed on the UAV and configured to sense an object in a subterranean environment based on a Ground Penetrating Radar (GPR) signal
Implementation Method 2
a SAR sensor disposed on the UAV and configured to receive the SAR signal
Data Source
AI summary
An aerial vehicle system for mapping an object buried in a subterranean surface, the aerial vehicle system including an aerial vehicle, an electronic sensor, a processor, and a memory. The memory includes instructions, which when executed by the processor, cause the system to receive a first input data set by the electronic sensor, the first input data set based on an electromagnetic signal and geographic location data, generate a raw image based on the first input data set, and compare the raw image to a calibration data set, the calibration data set based on material calibration data. The material calibration data is based on unique spectral reflection patterns of an object in a controlled environment at predefined heights and subterranean conditions.