An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition

Abstract

Indoor fire accidents have become increasingly common in recent years. More and more firefighting robots have been designed to solve the problem of fires. However, the indoor environment is very complex, with high temperatures, thick smoke, more turns, and various burning substances. In this study, a firefighting robot with autonomous inspection and automatic fire-extinguishing functions intended for use in unknown indoor environments was designed. Considering water’s poor efficiency and its inability to extinguish some combustion materials, other fire extinguishers were applied to design the extinguishing system. The robot can install four different extinguishers as required and select the appropriate fire extinguisher to spray it automatically. Based on the Cartographer SLAM (simultaneous localization and mapping) theory, a robot map-building system was built using Lidar scanners, IMU (inertial measurement unit) sensors, encoders, and other sensors. The accurate identification and location of the fire source were achieved using an infrared thermal imager and the YOLOv4 deep learning algorithm. Finally, the performance of the firefighting robot was evaluated by creating a simulated-fire experimental environment. In an autonomous inspection process of the on-fire environment, the firefighting robot could identify the flame in real-time, trigger the fire-extinguishing system to carry out automatic fire extinguishing, and contain the fire in its embryonic stage.