Research On Wireless Location Method Of Mine Locomotive

With the continuous progress and development of society,traditional mining equipment can no longer meet people's requirements for safety,reliability,and intelligence.Therefore,the current demand for modernization of these mining equipment is very urgent.The transportation of mine locomotives is an extremely important part,which is also prone to failure.Therefore,it is necessary to track the position of mine locomotives.At present,the positioning methods of domestic mine locomotives still have the problems of low accuracy,high cost,and susceptibility to interference.In response to these problems,this paper uses a combination of odometer,inertial measurement unit(IMU)and Wi Fi location fingerprint to reduce the cost of the positioning system and improve the reliability and accuracy of target positioning.The main work of this article has the following points:(1)In this paper,by constructing the RSSI logarithmic distance loss model and using ray tracing simulation technology,simulation data is generated,and simulation experiments is performed on five position fingerprinting algorithms(LR,SVM,RF,MLP,KNN).After analyzing and comparing the experimental results,the K nearest neighbor(KNN)algorithm is selected,and the hyperparameters of the algorithm are further optimized.However,in the online matching positioning stage,the occasional error of using the weighted KNN algorithm for position positioning is still large.Therefore,it is necessary to further integrate the odometer and IMU sensor data of the locomotive to improve the accuracy of the positioning result.(2)At present,both rail locomotives and trackless locomotives are mainly wheeled locomotives in the mine,so this paper mainly takes wheeled robot as the research target model.Firstly,based on the kinematic characteristics of the mine locomotives and the odometer model,a linear-circular approximation model is established.And then the IMU model and the improved complementary filtering algorithm(adaptive complementary filtering)are combined to obtain the trajectory of the locomotive,and the positioning effect of the scheme is verified by experiments.However,this method will cause errors to accumulate over time.In response to this problem,an extended kalman fusion positioning algorithm based on odometer,IMU and Wi Fi location fingerprint is further proposed.(3)In order to verify the positioning effect of the extended kalman positioning fusion algorithm of odometry,IMU and Wi Fi fingerprints in practice,an experimental platform for positioning system is designed and built,and the positioning effect of the fusion algorithm is measured and analyzed.Collect the RSSI signal through the mobile phone APP,and make a location fingerprint database from it.Then,the data of the speedometer,IMU and Wi Fi are acquired through the experimental vehicle platform,and the host computer system is uploaded.The extended kalman fusion algorithm in the host computer is used to obtain the final positioning result.Compared with other positioning devices,the Wi Fi module used in the positioning system has the advantages of low cost and wide communication range,so it can effectively reduce the cost of deploying the terminal.The final positioning results show that compared with the separate Wi Fi positioning method,the contingency error is greatly reduced,and the accumulated error is basically eliminated compared with the inertial sensor positioning alone.In summary,this positioning method can effectively reduce the system cost and has higher reliability and accuracy.