IMPLEMENTATION OF UNDERGROUND MINING ROBOT USING MACHINE LEARNING

Abstract: Underground mining operations pose significant safety risks, making early detection of hazards and real-time monitoring of worker health crucial. This abstract presents a novel Undermining Detection Robot (UDR) designed to enhance safety in underground mining environments. The UDR employs Arduino microcontrollers to interface with a suite of sensors, including metal sensors, fire sensors, gas sensors, ultrasonic sensors, and moisture sensors. These sensors provide real-time data on potential hazards such as gas leaks, fires, and unstable underground conditions. Additionally, the robot incorporates a water pump to address moisture related issues that may arise in mining operations. The UDR is equipped with an ESP32 CAM module, stream video from the mining site. This feature enhances remote monitoring and situational awareness for mine operators. The data collected from these sensors and the ESP32 CAM are transmitted to the cloud-based IoT platform, ThingSpeak, for real-time data analysis and visualization. The UDR also integrates sensors for monitoring personnel health parameters, including heartbeat, temperature, and Spo2 levels. This functionality ensures that workers' well-being is constantly monitored, and any anomalies or emergencies are promptly detected. The final layer of innovation in this system is the application of machine learning using Python. The collected data is analyzed using machine learning algorithms to predict potential safety hazards or health related issues. These predictions are then used to trigger immediate responses or alert personnel to take appropriate actions.

Keywords: UDR, sensors, ESP32CAM , Thingspeak ,ESP32,Arduino meg