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

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.