GPS Navigation and Location Method for Outdoor Firefighting Robot Based on Kalman Filter

With the continuous development and maturity of artificial intelligence sensing, positioning, navigation, and driving technologies, outdoor firefighting robots have been applied in more and more scenarios, and even in outdoor automatic tracking and firefighting. However, due to the frequent operation of outdoor firefighting robots in complex environments such as the wilderness, forests, and mountain slopes, they may encounter issues such as uneven ground, bumps, and overgrown weeds. This puts stricter requirements on the positioning technology of outdoor firefighting robots, resulting in GPS offset, with serious potential errors of over 50m. Additionally, in outdoor environments, it is difficult to deploy differential base stations, and it is even more difficult to obtain RTK data. Therefore, in response to the shortcomings of poor positioning accuracy and susceptibility to external environmental interference of outdoor firefighting robots, this paper proposes a GPS navigation and positioning method for outdoor firefighting robots based on kalman filter. Through this method, GPS can be subjected to amplitude limiting filtering and kalman filtering, so that GPS data with severe drift is limited to within 0.8m and the error is limited to 1%. By designing the Q and R matrix parameters of the kalman filter simultaneously, the algorithm performance can be improved. After simulation and experimental testing, the proposed algorithm has a strong limiting effect on GPS drift. It can limit GPS data to a normal range and can be used in the field of outdoor firefighting robots. Whether it is overgrown with weeds or crossing forests, it can operate normally and achieve its function.