Control System of Fire Rescue Robot for High-Rise Building Design

In recent years, with the steady and rapid growth of the national economy, various industries have developed rapidly and the resulting fire accidents have also been on the rise. This study mainly discusses the control system of a fire rescue robot for a high‐rise building design. Robot fire detection method involves obtaining the video image of the fire detection area through a high‐definition camera, performing image preprocessing on the current frame image of the video in the robot operating system to obtain the image; and performing the flame area segmentation based on the Ohta color space and the Otsu threshold segmentation algorithm on the image, to obtain the segmented image; through the interframe difference method, the preprocessed image of the previous frame is subtracted from the current frame image and the moving area of the image is segmented to obtain the segmented image; the obtained segmented image is combined with the segmented image in the robot operating system and images are intersected to obtain a segmented image with the characteristics of flame motion; based on other characteristics of the flame, flame recognition is performed on the area in the segmented image. Fires in high‐rise buildings are increasing gradually, which seriously endangers the safety of human life and property. The firefighting robot and its control system play an important role in the fire protection of high‐rise buildings. The purpose of this study is to analyze the disaster relief effect of a firefighting robot and control system in high‐rise buildings. In this study, we use MATLAB software to model and simulate the rescue situation of a firefighting robot in high‐rise buildings. The fuzzy control system established in the fuzzy logic toolbox of MATLAB can easily replace human field work and can change the control rules and membership function in the FIS editor. The results show that the rise time and adjustment time of the system are basically the same under the condition of variable load stiffness. The maximum overshoot is 0.59%, and the steady‐state error is 0.19%. The maximum overshoot is 1.0148%, and the stability error is 0.46%. It is concluded that the expert PID algorithm is efficient and practical. It can be concluded that the robot position control system adopts PID control algorithm, and the attitude control system adopts expert PID control algorithm. This research provides some value for the development and design of firefighting robots in high‐rise buildings in the future and also brings important significance.