Research On The Traceability Method Of Indoor Hazardous Gas Based On Active Olfaction

With the rapid development of industrialization,the application of hazardous gases brings convenience to the society and potential danger at the same time.The leakage of dangerous gas not only endangers the safety of people’s lives and property,but also pollutes the environment.Therefore,the research of gas source localization is of great practical significance in detecting the source of gas leakage in time and stopping the further expansion of the disaster.Due to the inflexibility and low efficiency of traditional traceability methods,in this paper,the mobile robot is used to collect information,and search the leakage source in the indoor environment through the algorithm based on robot smell.Firstly,the study of gas diffusion law helps to develop a gas source localization search strategy.In this paper,the most widely used Gaussian diffusion model is selected after comparing other commonly used gas diffusion models,and the selection of parameters in the Gaussian diffusion model is determined.After that,the Gaussian diffusion model is simulated in MATLAB platform,and simulation experiments are conducted by changing the plane height and wind speed size.The effects of different plane heights and wind speed sizes on the gas diffusion distribution law are analyzed according to the simulation results.Secondly,in order to solve the problems of premature convergence and easy to fall into local optimization in the process of plume tracking,an improved particle swarm optimization algorithm combining nonlinear decline of inertia weight and asynchronous change of learning factor is proposed.At the same time,second-order oscillation is introduced to increase the diversity of particle population.By balancing the ability of global search and local optimization of particle swarm optimization,the algorithm can accelerate the convergence speed of particles without affecting the accuracy of results,and promote the particles to quickly converge to the global optimization.Then the test function is simulated with different algorithms,and the simulation results show the effectiveness of the improved particle swarm optimization algorithm.The algorithm with good simulation results and the improved particle swarm optimization algorithm proposed in this paper are combined with Gaussian diffusion model to simulate the gas source location.The experimental results show that the improved particle swarm algorithm has better robustness and can effectively improve the convergence accuracy of localization results.Finally,the robot simulation olfactory experiment platform is built by designing hardware circuit module,and alcohol,which is harmless to human body and environment,is chosen instead of dangerous gas for the experiment.The gas sensor is calibrated to ensure that the concentration information collected by the gas sensor corresponds to the real concentration information.The smoke plume is artificially created in the closed indoor environment by using fan and humidifier,and the wind speed of the smoke plume environment is measured to ensure that the indoor environment can basically meet the conditions of Gaussian diffusion model.The experiments of gas source localization are carried out by using the improved particle swarm algorithm through multiple mobile robots in the built plume environment,8 out of 10 repeated experiments were successfully located,and the average positioning error of the experimental results is 0.379 m.The final experimental results show that the algorithm proposed in this paper is feasible and can complete the gas source location experiment with a high success rate.