AI-Based Autonomous Fire Detection and Extinguishing Robot: Design, Implementation, and Performance Evaluation

This paper has presented the complete design, implementation, and experimental validation of an AI-based autonomous fire detection and extinguishing robot intended for indoor deployment. The system achieves effective fire discrimination through a multi-modal sensor fusion architecture combining YOLOv5 visual classification on a Raspberry Pi inference board with environmental corroboration from MQ-2, DHT22, and infrared flame sensors, managed by a weighted fuzzy aggregation controller. Autonomous navigation employing an ESP32 actuation controller and HC-SR04 ultrasonic obstacle avoidance enables the robot to navigate furnished indoor environments and reach fire sources with a mean response time of 7.4 seconds, suppressing 93.3% of test fires within a single pump activation cycle. Comparative evaluation confirms a 5.2× improvement in response time and a 26-fold reduction in false suppression activations relative to a representative conventional passive smoke detector. These results validate the core thesis that integrating AI-driven computer vision with multi-modal sensor fusion and autonomous mobility creates a fire safety system qualitatively superior to existing fixed-installation approaches across the critical dimensions of detection speed, detection specificity, and autonomous intervention capability. The work demonstrates that commercially available embedded computing platforms now offer sufficient computational resources for real-time deep learning inference in robotic safety applications, lowering the barrier to practical deployment of autonomous fire response systems. As AI models continue to improve in efficiency and embedded hardware continues to advance in performance, the transition from research prototype to production-grade first-responder robotics for fire safety appears both technically feasible and socially necessary. The authors anticipate that systems of this type will, within the near future, become standard safety infrastructure in high-risk industrial and institutional settings globally.

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