Research On Remote Control And Path Planning Algorithm Of Forest And Grass Fire Fighting Robot

With the rapid development of robot technology,using fire robots to replace manual tasks to complete forest and grass resources patrol and fire protection has become an important development direction of intelligent forest and grass equipment.In the preliminary research,the research team initially completed a forest and grass fire fighting robot NEFU-F1 based on a crawler chassis.This research focuses on the following four aspects of intelligent optimization of forest and grass fire fighting robot research:(1)Based on the hardware platform of the existing remote control box,complete the development of the NEFU-F1 remote control system program for the forest and grass fire fighting robot,and achieve real-time remote control of the robot.Research and use the Qt Creator development framework to develop infrared thermal imaging and visible light image display interfaces,call Baidu Maps’ API to display maps within the interface for robot positioning and navigation,and use serial communication to achieve the transmission and display of vehicle body and peripheral sensor information.(2)Aiming at the phenomenon of GPS positioning errors in path planning for forest and grass fire fighting robots,a GPS/ODO combined positioning algorithm based on unscented Kalman filtering is proposed to achieve more accurate positioning control.The feasibility of the algorithm proposed in the study is verified by comparing the actual trajectory,filter trajectory,and observation trajectory before and after the improvement through experiments.(3)Combining the path planning problem of the two stages of forest and grass fire fighting,namely,rush to the fire site and afterfire patrol,this paper compares and analyzes different algorithms.During the process of going to the fire site,the A * algorithm is selected for path planning of the robot.The weighted Manhattan distance is introduced into the heuristic function of the algorithm to improve the operational efficiency of the algorithm,and a penalty function and reward factor are introduced to reduce the number of turns of the robot.After the open fire is extinguished,the internal spiral algorithm is used for traversing and patrolling the remaining fire.Based on the thermal radiation principle of forest and grass fire sites,a thermal radiation factor is introduced to conduct gradient approach patrols on the fire site to reduce the impact of high temperatures on the safety performance of the fire robot.The two improved path planning algorithms are tested to verify the feasibility of the improved algorithms in the study.(4)Field experiments of fire fighting processes are conducted in two environments,namely,grassland and forest.The main purpose is to evaluate the usability of remote control systems,the accuracy of positioning algorithms,and the accuracy of fire path planning algorithms.In a grassland environment,the remote control system can achieve friendly remote display and control purposes,achieve accurate positioning,and preliminarily implement fire path planning;In a forest farm environment,remote control systems can achieve friendly remote display and control purposes,and can achieve accurate positioning.However,during the path planning process,due to differences in geology and geomorphology,there is a certain deviation between the results of on-site experiments and simulation experiments.The research results show that the remote control system developed in the research can complete the remote control of NEFU-F1.The proposed GPS ODO combined positioning algorithm can improve the positioning accuracy of the robot.The improved A * algorithm and inner spiral algorithm have improved the efficiency and safety of the fire robot in forest and grass fire path planning to a certain extent,Therefore,this study has an important role in promoting the development of the intelligent forest and grass fire fighting robot NEFU-F1 from semiautomatic to fully automated,and has certain reference significance for the research of other intelligent robot control technologies and algorithms in the agricultural and forestry field.