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Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework.

Povendhan Palanisamy¹, Rajesh Elara Mohan¹, Archana Semwal¹

¹Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This summary is machine-generated.

This study introduces an AI robot for remote drain inspection, improving safety and efficiency. The system accurately detects concrete defects and creates detailed maps for better maintenance.

Keywords:

computer vision deep learning defect inspection drain inspection mapping reconfigurable robot

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Area of Science:

Robotics and Automation
Artificial Intelligence
Civil Engineering

Background:

Manual drain inspection is hazardous, inefficient, and prone to errors.
There is a need for automated, remote solutions for drain maintenance and assessment.

Purpose of the Study:

To develop and evaluate an AI-enabled robot-assisted framework for remote drain inspection and mapping.
To integrate deep learning models for concrete defect detection within an Internet of Robotic Things (IoRT) framework.

Main Methods:

Utilized a reconfigurable robot, Raptor, integrated with a four-layer IoRT.
Trained Faster RCNN deep learning models (ResNet50, ResNet101, Inception-ResNet-v2) for concrete defect classification using transfer learning.
Employed Simultaneous Localization and Mapping (SLAM) techniques for robot navigation and map generation.

Main Results:

The AI-powered robot demonstrated stable maneuverability and accurate mapping/localization in various drain environments.
Trained deep learning models effectively detected six typical concrete defect classes.
A SLAM-based defect map was successfully generated by fusing defect detection with lidar-SLAM data.

Conclusions:

The AI-enabled robot-assisted framework offers a safe, efficient, and accurate solution for remote drain inspection.
The developed system facilitates effective drain maintenance through precise defect mapping and localization.