Research On Indoor AGV Location And Path Planning Algorithm

With the vigorous development of intelligent factories,intelligent manufacturing,intelligent storage,logistics,and other industries,automated guided vehicles(AGV)have a wide application prospect.Human and material resources have been required in traditional mechanized work.AGV gradually replaces most of the labor force because of its advantages of saving costs,improving efficiency,high safety,and easy management.In the process of AGV operation,obstacle avoidance and positioning while planning an efficient path have always been research hotspots.Therefore,in order to improve the quality of AGVs in the operation process,this paper does the following research work on AGV visible light localization and path planning algorithms:Aiming at the problem of AGV visible light location,this paper mainly includes two stages: In the offline stage,optimization of the SVT algorithm is proposed in this paper.A matrix completion algorithm is used to derive the light signal intensity relationship between the reference point and other location points and consequently estimate the RSS value of the unknown point.Aiming at the problem that the positioning accuracy is reduced due to light occlusion in the matrix completion algorithm,the singular value adaptive assignment is introduced to recover the matrix information accurately and quickly.In the online stage,in order to accurately find the unknown target location point,the improved WKNN algorithm is used to select the RP by analyzing the physical distance of RSS instead of the spatial distance.At the same time,the improved Euclidean distance is proposed as the choice of its distance measurement.Finally,two different map environments are set up in MATLAB2017 a to simulate and analyze the feasibility of the improved WKNN algorithm.An improved A* algorithm is proposed in this paper to solve the problem of AGV path planning.Firstly,a grid map of a rectangular coordinate system is constructed according to the real indoor environment(on the grid map,the starting point and target point of AGV are set).Then,based on the 4-way or 8-way node expansion used in the traditional A* algorithm,a 16-way search method is proposed to improve the grid search efficiency,and then the obstacle information and safety factor are considered in the evaluation function of the A* algorithm.In order to improve the tracing efficiency,the redundant points in the common line and corners of the path are focused on.Three different map environments are set up to simulate and compare the advantages of the improved A* algorithm,the traditional A* algorithm,ant colony algorithm,and D*algorithm in terms of path length,planning time,and path smoothness.Building a positioning experimental system according to the improved algorithm in this paper,which mainly includes two parts: the LED transmitter and the AGV receiver.and verifying whether the AGV can reach the designated positioning grid area through two sets of experiments designed.Figure [55] Table [13] Reference [83]...