Autonomous Fire Detection and Suppression Robot using Machine Learning Algorithms

Abstract

Fire incidents continue to pose substantial threats to human safety, infrastructure, and valuable assets. Traditional fire-monitoring systems commonly use smoke and thermal sensing techniques and frequently depend on manual action, which can delay emergency response and worsen fire-related losses. This introduces an autonomous robotic platform that combines embedded hardware with machine-learning-based fire identification and suppression to provide rapid and intelligent fire response. The proposed system utilizes flame, smoke, and thermal sensors for continuous observation of the surrounding environment. The collected measurements are analysed through machine-learning models such as Decision Tree and Random Forest classifiers to distinguish fire conditions while reducing incorrect detections. When the fire is detected, the robot autonomously navigates toward the affected location by while employing ultrasonic sensing to identify and bypass obstacles. A relay-controlled water pump is then activated to suppress the fire without direct operator involvement. With the help of artificial intelligence, autonomous navigation, and automated fire suppression strengthens response efficiency and safety while limiting personnel exposure to hazardous conditions in hazardous environments. The proposed solution offers an economical and dependable solution for deployment in laboratories, industries, warehouses, and other high-risk areas