Research On Access Strategy And Path Planning Based On Planar Movable Stereo Garage

As a kind of intelligent stereo garage,planar movable stereo garage has the advantages of convenient construction,compact overall design structure,space saving and low investment cost,etc.It is more suitable for large-scale development and has good market prospects.The planar movable stereo garage studied in this paper relies on the AGV car on the ground plane,the vertical operation of the intermediate main lift,the horizontal operation of the handling trolley and other handling equipments to realize the intelligent vehicle access service.In order to improve the overall efficiency of vehicle access and reduce the time of vehicle access,this paper starts from two aspects.On one hand is to find suitable vehicle access strategies for the garage.On the other hand,for the path planning of AGV car on the ground plane,the appropriate algorithm is selected to solve static obstacle avoidance.The main research contents are as follows:(1)Combining the specific access vehicle process of planar movable stereo garage,four access vehicle strategy time mathematical models of it was established.And considering different vehicle access situations in flat peak,storage peak and pick-up peak periods,the shortest time-consuming vehicle access strategy was selected through MATLAB calculation and analysis.It could improve the garage access efficiency and reduce equipment energy consumption.(2)The modeling method of path planning problem was clarified.The grid method was selected to model the running environment of AGV car,and the twodimensional abstract model that can be processed by computer was generated.The related algorithms to solve the path planning problem were analyzed,which laid the foundation for the following selection of reasonable path optimization algorithms.(3)By analyzing the advantages and disadvantages of genetic and traditional grey wolf optimizer,an improved grey wolf optimizer was designed.Three aspects were improved.Firstly,a self-defined reverse learning strategy was used to generate the initial population to ensure the quality of the initial solution.And then,a two-stage nonlinear convergence factor was proposed to balance the global search and local development capabilities of the algorithm.Finally,the crossover and mutation search strategies of genetic ideas were used to improve the convergence speed of the algorithm in the later stage.(4)On maps of different obstacle types,the path maps generated based on the above three algorithms were compared through MATLAB simulation experiments,which proved that the improved grey wolf optimizer was stable in solving path planning problems.And it was applied to the path planning of AGV car on the ground plane of planar movable stereo garage.By changing the starting and ending points of the path,a better path could be generated.It was proved that it was feasible and effective in solving the obstacle avoidance problem of garage static path.