Research On Path Planning And Fire Extinguishing Agent Selection Method Of Robot In Fire Rescue

In recent years,tens of thousands of fire accidents occur every year in China,which seriously affects people’s normal production and life.With the rapid development of robot technology,robots are favored by all walks of life because of their intelligence and high efficiency.In complex fire environments,robots can replace rescue workers to complete dangerous tasks,which can not only effectively protect the lives of rescue workers,but also improve the efficiency of the rescue team.Therefore,the participation of mobile robots in fire rescue has important research significance.This thesis mainly focuses on optimization research on two aspects of robot path planning and fire extinguishing agent selection in fire rescue.The main work is as follows:(1)The main content of fire rescue is analyzed.Study the fire rescue path planning algorithm,especially the ant colony algorithm.And the ontology model,logical reasoning,and SWRL rule technology are studied.(2)In order to solve the problem of multi-factor optimal path planning for mobile robots in fire rescue,this thesis proposes a rescue path planning method based on improved ant colony algorithm.While improving the global pheromone update strategy,it considered the main factors affecting the time for the mobile robot to reach the rescue point,such as the path length,the number of turns,and the size of the slope,and allocated the pheromone amount on each path according to the multi-factor comprehensive index,to guide the ant to the comprehensive optimal path.Through simulation examples and comparison with similar methods,the results show that the performance of the method in this thesis is greatly improved after considering multiple factors,has better global search ability and convergence speed,and can improve the efficiency of mobile robots in fire rescue.(3)Aiming at the selection of fire extinguishing agent by robots in fire scene,a method of selecting fire extinguishing agent based on ontology knowledge reasoning is proposed.Firstly,the ontology model is constructed according to the basic case and experimental data.Secondly the similarity measurement is carried out from the semantic and numerical feature items to establish the relationship between the basic case and the experimental data.Finally,based on ontology and semantic web rule language,the selection rules of ontology general,semantic feature and numerical feature are formulated to realize the knowledge inference of fire extinguishing agent selection from multi-source data.Experimental results show that the semantic difference between basic cases and experimental data can be eliminated by similarity measurement,and inference based on semantic web rules language can guide the robot to select suitable extinguishing agent scheme in fire rescue,which provides a new idea for intelligent selection of extinguishing agent based on multi-source data.The thesis work can provide methods for path planning and fire extinguishing agent selection of robot in fire rescue,and improve the rescue efficiency.