Strandbeest Coal Mine Robot for Detection of Hazardous Objects and Gases

Abstract

Worker safety in coal mines is of utmost significance, given the high likelihood of hazards in mining processes, ranging from harmful gases and unforeseen objects. Here, we report the design of an autonomous robot for detecting hazards such as gases and objects in coal mines. Based on state-of-the- art sensors, comprising gas sensors for detecting methane, carbon monoxide, and oxygen content, as well as infrared cameras to detect potential gas leaks and dangerous atmospheric pressure, temperature, and oxygen level conditions, and physical obstacles endangering worker lives, therobot can detect hazards automatically. With real-time data transmission capabilities for reporting its observations to centers in charge, it can respond instantly in emergency conditions. Its mobility system, capable of accessing tight and risky places, can detect hazards in difficult-to-reach and risky locations, improving the efficiency and effectiveness of hazard identification. This robot is expected to greatly increase worker protection through reduced exposure to risky scenarios and offer an active, sturdy approach to hazard identification and risk mitigation in coal mines[1].

Introduction

Coal mining remains crucial for energy but is highly risky due to toxic gas exposure, debris collapse, and equipment accidents. Traditional safety measures rely on manual inspection, which is slow, error-prone, and dangerous for workers. Advances in robotics and sensor technology offer better safety solutions.

The paper introduces the Strandbeest robot, an autonomous, rugged platform designed to navigate difficult underground terrains using a leg-based locomotion system. It continuously monitors hazardous gases like methane and carbon monoxide using gas sensors and detects physical hazards with ultrasonic sensors, infrared cameras, and object recognition. Data is sent wirelessly in real-time to remote operators for prompt hazard response, reducing human exposure to danger.

Current safety systems have limited coverage, poor mobility, and inadequate integration, often requiring manual intervention and having scalability issues. In contrast, the Strandbeest offers full autonomy, robust sensor fusion, and adaptability to harsh mining conditions.

The robot’s core components include Arduino Uno (controller), ESP32-CAM (camera), L298N motor driver, MQ9 gas sensor, ultrasonic sensors, and servo motors. It uses onboard AI and mapping (LiDAR/ultrasonic) for navigation and hazard detection. The system enables continuous, reliable monitoring, timely alerts, and safer mining operations with less downtime.

Overall, the Strandbeest represents a transformative technological solution, improving coal mine safety, operational efficiency, and worker protection by integrating advanced robotics, real-time data processing, and smart environmental sensing.

Conclusion

In this project, Theo Jansen\'s mechanical walking structure has been used to design a Strandbeest robot. It uses Arduino Uno, gas sensors (such as MQ series), and a camera module for identifying harmful gases and obstacles in coal mines. With its legged movement, which is inspired by Theo Jansen\'s mechanical walking structure, the robot can walk over rugged and uneven coal mining surfaces within which wheeled robots can get stuck. The gas sensors detect harmful gases such as carbon monoxide and methane in the surroundings, while a camera module offers real-time visual feedback for navigating and detecting obstacles. It is compact in size, economical in cost, and meant for remote use within harsh and risky underground environments. This shows the viability of using biogeometry -based mechanical structures integrated with embedded systems for industry-level safety purposes. Autonomous path planning, wireless data transmission using IoT, and machine learning-based algorithms for better object and gas identification can be further included in future improvements for better system robustness and efficacy[12].

References

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Copyright

Copyright © 2025 Dr. V. Rajya Lakshmi, Prabhas Akhil Reddy, Bandaru Suvarna, Gandi Uday Rahul, Dikkala Venkat Sai Ramesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.