Collaborative Pipe Robots Using Structured Light for 3D Asset Mapping
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Solution Overview
Problem
Conventional systems for mapping underground utility infrastructure, particularly in smooth surfaces like internal PVC pipe walls, struggle to accurately locate assets and identify anomalies without disrupting utility services, and lack cost-effective and accurate methods for identifying underground infrastructure anomalies.
Innovation Solution
A collaborative robotic system comprising two autonomous robots with extendable legs, a camera module, laser module, and processing module, which projects a structured light pattern, collects images, and processes data to generate a 3D mapping of underground assets, using advanced training models to identify features and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mapping systems are used for smooth surfaces like internal PVC pipe walls, then the system structure remains simple, but the ability to accurately locate assets and identify anomalies deteriorates
Solution Approach 1:
A laser module projects structured light patterns onto the smooth pipe interior surfaces, serving as an intermediary to create artificial features that the camera module can detect and use for accurate 3D mapping and anomaly identification
Solution Approach 2:
The system transitions from 2D camera images to 3D point cloud mapping by combining multiple image perspectives and laser distance measurements, enabling accurate localization of assets and anomalies in three-dimensional space
2Measurement precision
If existing systems collect data on internal features and defects, then measurement capability is improved, but service interruption to users worsens
Solution Approach 1:
The autonomous robotic system navigates and operates independently within the pipe, collecting its own data without requiring external service intervention or user disruption, thereby maintaining continuous utility service
Solution Approach 2:
The system replaces traditional mechanical inspection methods that require service disruption with autonomous robotic navigation and optical sensing, enabling data collection during normal operation
3Measurement precision
If conventional methods are used for identifying underground infrastructure anomalies, then the system remains simple, but identification accuracy and objectivity deteriorates
Solution Approach 1:
The processing module uses machine learning models trained on anomaly data to provide automated feedback-based classification and identification of defects, improving accuracy and objectivity while reducing manual inspection requirements
Solution Approach 2:
The system transforms raw image and laser data into standardized 3D point cloud representations with consistent coordinate systems and scaling, enabling objective comparison and analysis across different pipe sections and conditions
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 accurate and efficient mapping and identification of underground assets and anomalies without service disruption, supporting the transition to zero-carbon alternatives like hydrogen and biomethane.
Implementation Method 1
a laser module designed to project a structured light pattern
Implementation Method 2
a camera module designed to collect images from the camera module
Data Source
AI summary
An autonomous collaborative robotic system for automatically detecting, locating, and mapping underground assets is provided. The collaborative robotic system includes two autonomous robots, each robot including a housing with extendable legs, a camera module, a laser module designed to project a structure light pattern on a pipe interior, a sensor module designed to collect images from the camera module, and a processing module designed to process and stitch data to generate a network of underground assets and identify one or more features within the asset network.