Multi-Sensor Robotic Mapping for Real-Time Pipeline Defect Detection
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Solution Overview
Problem
Conventional methods for detecting features and defects in enclosed spaces like pipelines rely on human intervention, leading to errors and inefficiencies. There is a need for an automated detection method with high accuracy that can be applied across multiple industries and domains.
Innovation Solution
A robotic system equipped with multiple sensors, including visual, infrared, LIDAR, and IMU sensors, that uses deep learning algorithms to detect features and defects in real-time. The system tracks the robot's position, recognizes features using sensor data, and creates three-dimensional representations for mapping purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human intervention methods are used for detecting features and defects, then the process is simple to implement, but the detection accuracy and efficiency deteriorate due to human errors and susceptibility to mistakes
Solution Approach 1:
The patent replaces the mechanical human inspection system with an automated robotic system equipped with multiple sensors (visual cameras, infrared cameras, LIDAR, ultrasonic sensors, gas sensors, moisture sensors, pressure sensors) and deep learning algorithms. This substitution eliminates human errors while achieving high detection accuracy through automated feature recognition and defect identification in pipeline environments.
2Adaptability or versatility
If multiple sensors are deployed for comprehensive data collection, then the detection capability and accuracy improve, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent implements a multi-functional robotic system that integrates multiple sensor types (visual, infrared, LIDAR, ultrasonic, gas, moisture, pressure sensors) into a single platform. This universal system can detect various features and defects across different pipeline conditions and environments, with deep learning algorithms processing data from all sensors to provide comprehensive inspection capabilities.
Solution Approach 2:
The patent merges data from multiple independent sensor sources into a unified detection framework. The visual camera, infrared camera, LIDAR, ultrasonic sensor, gas sensor, moisture sensor, and pressure sensor all feed into a common deep learning processing system that correlates their data to identify features and defects, reducing overall system complexity through integrated data fusion.
3Productivity
If automated deep learning algorithms are used for feature recognition, then the detection efficiency and accuracy improve, but the computational requirements and processing time worsen
Solution Approach 1:
The patent employs deep learning algorithms that have been pre-trained offline on large datasets of pipeline features and defects. During actual inspection, the pre-trained models perform rapid inference on sensor data, significantly reducing real-time computational energy consumption while maintaining high detection efficiency and accuracy for feature recognition and defect classification.
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
The system achieves accurate and efficient detection of features and defects in enclosed spaces, reducing human error and improving inspection efficiency across various industries and domains.
Implementation Method 1
one or more spatial distance sensors such as LIDAR
Implementation Method 2
Other systems utilize infrared cameras to detect temperature variations within the scanned environment
Implementation Method 3
The IMU may include accelerators and gyroscopes
Implementation Method 4
The IMU may include accelerators and gyroscopes
Data Source
AI summary
Robotic systems and associated methods are described herein. The robotic system may collect measurements from various sensors corresponding to motion of the robotic system, the surrounding environment of the robotic system, or both. The robotic system may generate measurement data based on the collected measurements. Measurements from a particular sensor may be processed in conjunction with different sensors of the robotic system, which may facilitate more accurate or more useful measurement data. The systems and methods of the present disclosure enable the detection, labeling, and locating of features in real time or near real time using the robotic system with little or no reliance on human interaction to detect and map the features. The disclosure provides enhanced accuracy and efficiency as it enhances the functionality and reduces the reliance on human detection of features.