Design Of A Fire-Fighting Robot For Industrial Applications

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.