【作者】 杨俊；

【导师】 林建国；

【作者基本信息】 武汉理工大学 ， 车辆工程， 2021， 硕士

【摘要】 在海运物流需求日益增长的背景下,智能化、自动化已成为港口发展的必然趋势,其中用于集装箱转运的自动导引车(Automated Guided Vehicle,AGV)是自动化集装箱港口(Automated Container Terminal,ACT)装备的重要组成部分。在ACT中,协同控制系统将集装箱转运任务和道路资源分配至AGV,同时路径规划根据分配内容规划路径。考虑大型AGV多载和重载性能虽然能够直接提升港口运行效率,但却增大了问题求解的复杂度。因此,为了进一步提高港口效率,本文以ACT场景下的大型AGV及其车队为研究对象,针对多载、重载工况下协同控制和路径规划问题,开展智能调度以及路径规划研究,主要内容如下:提出多载AGV调度的多目标优化模型。从对AGV多载特性以及港口集装箱转运流程的分析出发,针对多载调度的特点以及传统调度方法的不足,建立多目标优化调度模型。对于动态、大批次任务问题,引入滚动时域优化和事件驱动混合的调度触发方法,保证调度模块满足动态响应环境变化的要求。实现多AGV协同全局路径规划。结合港口场景特点,分析常用的环境建模方法以及路径规划方法,选取拓扑地图和LPA*的匹配方案。为了合理分配道路资源以及避免冲突,建立时间窗模型作为道路交通状况预测和AGV冲突检测的工具。并以时间窗模型改进拓扑地图以及LPA*算法,从而实现多AGV协同全局路径规划。建立大型重载AGV局部路径规划框架。通过分析AGV重载特性和港口水平运输区特点对局部路径规划的要求,框架分解局部路径规划为轨迹形状规划和速度规划两部分。两部分均以采样搜索生成初始解,并针对初始解进行二次规划优化。框架考虑了AGV轨迹可行性等要求,并根据其他AGV控制策略生成最终轨迹。搭建多AGV协同控制仿真系统并进行仿真分析。通过分析ACT集装箱转运工作场景特征,整合验证上述AGV协同控制系统所需的模块,并以集装箱从驳船转运至堆场为任务场景进行了仿真实验。结果表明,AGV协同控制和路径规划算法具有较好的可行性和稳定性。港口大型多载AGV协同控制和路径规划方法的相关研究对ACT运行效率的提升和优化,以及实现传统港口智能化升级具有一定的理论价值和实际指导意义。更多还原

【Abstract】 With the increasing demand for maritime logistics,intelligenlization and automation have become inevitable trends in container terminal development,and Automated Guided Vehicles(AGV)are key transferring equipment in automated container terminals(ACT).In ACT,the cooperative control system assigns container transfer tasks and road resources to AGVs,while the path planning figures out path according to the assigned content.Considering the multi-load and heavy-load characteristic of large AGVs can directly improve the efficiency of container terminal,although it increases the complexity of the problem.Therefore,in order to further improve container terminal efficiency,this paper takes large AGVs and their fleets in ACT as research objects,and carries out intelligent dispatching as well as path planning for the cooperative control under multi-load and heavy-load working conditions.The main research content is as follow.A multi-objective optimization model of multi-load AGV dispatching is proposed.With the analysis of AGV multi-load characteristics and container transfer process,this model is established to address the characteristics of multi-load and the shortcomings of traditional dispatching methods.For dynamic and large-scale task dispatching problems,a hybrid dispatching trigger method,which combining with rolling-time-domain optimization and event-driven,is introduced to ensure that the dispatching module meets the requirements of dynamic response to environmental changes.Realize multi-AGV cooperative global path planning.Combining with the characteristics of the container terminal scenario,the common environment modeling methods and path planning methods are analyzed,and then the matching scheme of topological map and LPA* is selected.In order to reasonably allocate road resources and avoid conflicts,a time window model is established as a tool for road traffic condition prediction and AGV conflict detection.And the topological map and LPA* algorithm are improved with the time window model,so as to realize the multi-AGVs cooperative global path planning.Establishing a local path planning framework for large heavy-load AGVs.By analyzing the requirements of local path planning by AGV heavy load characteristics and port horizontal transport area,the local planning problem is decoupled into trajectory profile planning and speed profile planning.This frame generates the initial solution by sampling and searching,and then performs quadratic programming optimization for the initial solution for trajectory profile and speed profile.It considers the smoothness and feasibility requirements of AGV trajectory and generates the final trajectory according to control strategies of the other AGVs.A multi-AGVs cooperative control simulation system is built and related experiments are conducted.By analyzing the characteristics of the automated port container transfer work scenario,the modules required to validate the above AGV cooperative control system are integrated,and simulation experiments are conducted with the scenario of container transferred from quay to yard.The results show that the AGV cooperative control and path planning algorithms have good feasibility and stability.The research related to the cooperative control and path planning methods of large multi-load AGVs in ACT has certain theoretical value and practical guidance significance for the improvement and optimization of ACT and the realization of intelligent upgrading of traditional ports.更多还原