移动机器人智能车场停车资源调度分配

Automated Guided Vehicles Intelligent Parking Lot Allocation Model under Peak Demand

当今社会“停车难”的问题日渐严峻,通过引入移动机器人(automated guided vehicle,AGV)构建自动化智能停车场迫在眉睫。针对AGV智能停车场可以自由选择终点这一特点,本文提出了一种停车资源分配方案的代价评价模型,将路径干涉概率引入代价评价模型;同时针对现有的停车资源分配方法对AGV运行情况运行不足从而造成浪费的情况,本文在现有的停车资源分配方法的基础上提出了根据距离交替分配以及基于遗传算法的整体规划分配两种停车资源分配方法。通过Plant Simulation软件对代价模型以及分配方法进行验证,仿真结果显示:在44个停车位,4辆AGV投入运输的情况下,通过降低停车代价将停车总时长提高了30.4%,并针对仿真过程对影响停车效率的因素进行分析。

The problem of“low efficiency and difficult management”of parking lots in commercial,civil aviation and automobile manufacturing industries in today's society is becoming increasingly serious,and it is extremely urgent to build an automated intelligent parking lot by introducing automated guided vehicles(AGV).In view of the feature that AGV intelligent parking lot can freely choose the end point,it proposes a cost evaluation model for the parking resource allocation scheme,which introduces the path interference probability into the cost evaluation model;Insufficient and wasteful situation,it proposes two parking resource allocation methods based on the existing parking resource allocation methods:alternate allocation according to distance and overall planning allocation based on genetic algorithm.The cost model and allocation method are verified by Plant Simulation software.The simulation results show that when 44 parking spaces and 4 AGVs are put into transportation,the total parking time is increased by 30.4%by reducing the parking cost.And according to the simulation process,the factors affecting the parking efficiency is analyzed.