Abstract: This article develops an intelligent firefighting robot with autonomous inspection and automatic fire extinguishing functions to cope with complex environments and emergency situations in fires. In terms of methodology, a tracked mobile carrier robot was designed based on the ROS system, combined with SLAM and deep learning object detection technology. The SLAM map was constructed by integrating sensor data such as LiDAR, IMU, and odometer, and flame recognition and localization were performed using thermal imaging and YOLO technology. In terms of SLAM, a Cartographer was selected and optimized to address the issue of composition errors in large-scale building environments. In addition, the YOLOv4-F flame detection model was proposed and transplanted to the ROS system to achieve automatic aiming function. The results indicate that the optimized SLAM algorithm effectively reduces composition errors in large-scale environments, as demonstrated by the YOLOv4-F model mAP@0.5 reaching 85.53%, with a real-time detection accuracy of 81% and a speed of 11 frames per second. In the experiment, the robot successfully achieved autonomous inspection and automatic fire extinguishing functions, which can accurately identify flames in smoke environment in real time and quickly extinguish initial fires, providing effective technical support and new ideas for the practical application of intelligent firefighting robots.

Key words: ROS system, firefighting robot, intelligence, SLAM, YOLO