International Journal of Science, Engineering and TechnologyAuthors: Yashraj Nayak, Om Prakash Sondhiya
Abstract: The escalating frequency and severity of fire incidents in industrial environments—spanning petrochemical refineries, warehouses, power generation facilities, and chemical manufacturing plants—demands innovative solutions that minimise human exposure to life-threatening hazards. This paper presents the systematic design, development, and experimental validation of an autonomous fire-fighting robot engineered specifically for industrial deployment. The proposed platform integrates a thermally insulated omnidirectional Mecanum-wheel chassis with an embedded multi-sensor array comprising a FLIR Lepton 3.5 infrared thermal imager, Hamamatsu UV flame sensors, a Velodyne VLP-16 three-dimensional LiDAR, and electrochemical gas detectors. A lightweight convolutional neural network, FireDetNet-v2, trained on 45,000 annotated industrial fire images, achieves a mean average precision (mAP@0.5) of 97.6% at 30 frames per second on an NVIDIA Jetson Orin NX compute module. A 150-litre onboard water–AFFF suppression module delivers agent at up to 12 bar through a two-degree-of-freedom pan-tilt nozzle gimbal, achieving a maximum throw range of 15 m. Simultaneous localisation and mapping (SLAM) via the Cartographer framework on VLP-16 LiDAR data enables autonomous navigation in GPS-denied, smoke-filled corridors. Fifty controlled fire trials spanning Classes A, B, and C across a purpose-built industrial mock facility yielded a 94% overall suppression success rate, with a mean detection latency of 5.2 s and a mean time-to-extinguishment of 41.7 s. The system satisfies IEC 61508 SIL-2 functional-safety requirements for the suppression interlock, IP67 environmental protection, and a minimum 50-minute mission endurance.
