3D Sensing Robot for Multi-Sensor Pipeline Defect Mapping
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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, uses deep learning algorithms to detect features and defects within enclosed spaces. The system tracks the robot's position, recognizes features using sensor data, and creates three-dimensional representations of the environment, aggregating data with a weighting algorithm to predict features present in the space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques with human intervention are used for detecting features and defects, then the process is simple to implement, but the detection accuracy is low and the process is susceptible to errors
Solution Approach 1:
The patent replaces manual human inspection with an automated robotic system equipped with multiple sensors (visual cameras, infrared cameras, LIDAR, IMU) and deep learning algorithms. This substitution eliminates human error and subjectivity in defect detection while maintaining systematic and repeatable inspection processes, directly improving measurement precision without requiring complex manual procedures
Solution Approach 2:
The robotic inspection system integrates multiple sensor types (visual, infrared, LIDAR, IMU) and processing capabilities (deep learning algorithms, 3D mapping, feature recognition) into a single multi-functional platform. This universal system can detect various types of defects across different pipeline conditions and environments, improving detection accuracy while consolidating what would otherwise require multiple separate inspection methods
2Reliability
If multiple sensors are deployed on the robot for comprehensive data collection, then the detection accuracy improves, but the device complexity increases
Solution Approach 1:
The patent combines data from multiple sensor types (visual cameras, infrared cameras, LIDAR, IMU) into a unified processing framework using deep learning algorithms. The system merges these disparate data streams to create comprehensive 3D representations and correlated feature maps of the pipeline, improving detection reliability by cross-validating findings across multiple sensor modalities while managing complexity through integrated processing
Solution Approach 2:
The patent introduces deep learning algorithms and 3D mapping processes as intermediary layers between the raw sensor data and the final defect detection results. These intermediaries process, correlate, and interpret the complex multi-sensor data streams, transforming them into meaningful feature representations that improve detection reliability while abstracting away the complexity of handling multiple sensor types
3Productivity
If automated deep learning algorithms are used for feature recognition, then the detection efficiency and accuracy improve, but the processing time and computational requirements increase
Solution Approach 1:
The patent employs deep learning algorithms that have been pre-trained on extensive datasets of pipeline features and defects. This preliminary training allows the system to rapidly recognize and classify features during actual inspection without requiring extensive real-time computation, improving inspection efficiency while minimizing processing time during field operations
Solution Approach 2:
The patent segments the inspection process into distinct stages: data collection by multiple sensors, 3D mapping and feature extraction using deep learning, and final defect classification. This segmentation allows computationally intensive deep learning operations to be applied selectively to extracted features rather than raw data, improving processing efficiency while maintaining high detection accuracy
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 real-time or near real-time, reducing human error and enhancing the precision of pipeline inspections across various industries.
Implementation Method 1
one or more spatial distance sensors such as LIDAR
Implementation Method 2
one or more IR cameras
Implementation Method 3
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.