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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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A modular, multi-sensor crawler robot for adaptive pipeline inspection: design and experimental validation.

Ahmed A Abd Eltwab¹, Ahmed Sameh²

¹Department of Mechatronics Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt. hihamadaali2001@gmail.com.

Scientific Reports

|January 8, 2026

Summary

An autonomous crawler robot was developed for petroleum pipeline inspection, enhancing safety and efficiency. This system enables real-time, multi-modal defect detection, reducing risks associated with aging infrastructure.

Keywords:

Autonomous navigation Crawler robot Gas leak detection In-pipe inspection Raspberry Pi Real-time defect detection Structural health monitoring Ultrasonic sensing

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Area of Science:

Robotics and Automation
Materials Science and Engineering
Petroleum Engineering

Background:

Aging petroleum pipeline infrastructure presents significant safety, operational, and environmental risks.
Common pipeline defects include cracks, corrosion, joint displacement, and deformation, leading to leaks and failures.
Traditional inspection methods using manual robotic crawlers are time-consuming and prone to errors due to human video review.

Purpose of the Study:

To design, fabricate, and validate an autonomous modular crawler robot for petroleum pipeline inspection.
To integrate multi-modal sensing capabilities for real-time defect detection.
To enhance pipeline maintenance through an adaptable and autonomous inspection solution.

Main Methods:

Development of an autonomous modular crawler robot powered by Raspberry Pi 4 and Arduino Mega.
Integration of a high-resolution camera, ultrasonic distance sensors, and gas detection sensors.
Experimental validation in laboratory and simulated field conditions, assessing navigation, obstacle detection, climbing ability, and battery endurance.

Main Results:

The crawler robot demonstrated autonomous navigation, real-time video streaming, and multi-sensor data fusion.
Achieved a maximum speed of 0.25 m/s, 91.2% obstacle detection accuracy, 45° climbing capability, and 80-minute battery life.
The system showed adaptability to varying pipe diameters and material conditions.

Conclusions:

The developed autonomous crawler robot offers improved adaptability, sensing integration, and autonomy compared to existing systems.
The system is a viable solution for preventive maintenance of petroleum pipelines.
Future extensions include AI-driven defect classification and Simultaneous Localization and Mapping (SLAM)-based navigation.