Research On Unmanned Vehicle Positioning Technology In Terminal

With the development of automated terminals,various types of loading and unloading carriers are gradually implementing unmanned operation management.In response to the complicated operating environment of outdoor wharfs,automated wharves are increasingly demanding efficient and fast operation of unmanned carriers,and the accuracy of operations is as high as centimeters.Wideband(UWB)positioning system is applied to terminal unmanned vehicle positioning.However,the terminal environment is highly dynamic,the position between the container and the unmanned vehicle is not fixed,and the multipath effect and non-line-of-sight(NLOS)transmission between the vehicle and the vehicle,the vehicle and the container are prone to occur.UWB positioning systems can no longer meet current needs.In order to improve the positioning accuracy of unmanned carrier vehicles and reduce the positioning error,this paper proposes strong anti-interference and positioning accuracy under the effective distinction between line-of-sight(LOS)and non-line-of-sight positioning environments.High outdoor UWB positioning algorithm.First distinguish the positioning environment,classify the distance data according to the classification and regression tree(CART)method based on ranging estimation,and select the LOS base station and NLOS base station from the data.Ranging values are positioned separately: 1)In the LOS environment,the Chan algorithm is used to locate and an error model is established based on the measurement data.To increase the positioning accuracy,the number of base stations is increased,and then the Chan algorithm is used to obtain the initial value and then the weighted centroid algorithm is used.In order to increase the accuracy of the initial value,a PCWCL localization algorithm based on particle swarm optimization(Particle Swarm Optimization(PSO))is used to obtain the initial value and then perform weighted centroid localization.The research results show that: under the LOS environment,the positioning accuracy of the PCWCL positioning algorithm is 8.98% higher than that of the PSO and Chan hybrid algorithm,and the positioning accuracy of the Chan algorithm is 48.83% higher;2)In the NLOS environment,the Chan algorithm and weighted centroid are susceptible to environmental influences.Therefore,the Taylor algorithm is used.Due to the high initial requirements of Taylor,a Whale Optimization Algorithm(WOA)Intelligently optimized Taylor positioning algorithm.A predator model of whale population was established by the WOA algorithm,and the problem of confirming the position of unmanned vehicles was converted into a global minimum problem of solving the error between the optimal whale position and the individual whale position.Concerning the problems of slow convergence rate,insufficient global optimization ability,and easy to fall into the local optimal solution of the traditional whale optimization algorithm(WOA),the contraction factor(CF)and simulated annealing algorithm(SA)were introduced respectively.Improve it,and finally use Taylor algorithm to determine the final position of the target.The results show that the positioning accuracy of the CSWT positioning algorithm in the NLOS environment is 28.12% higher than that of the WOA and Taylor hybrid algorithm,and 52.02% higher than the positioning accuracy of the Taylor algorithm.In summary,the UWB-based terminal unmanned vehicle positioning algorithm studied in this paper can provide a new idea for high-precision positioning and navigation of outdoor terminal unmanned vehicles.