Application of Automated Guided Vehicles in Smart Automated Warehouse Systems:A Survey

Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.

A greedy path planning algorithm based on pre-path-planning and real-time-conflict for multiple automated guided vehicles in large-scale outdoor scenarios

With the wide application of automated guided vehicles(AGVs) in large scale outdoor scenarios with complex terrain,the collaborative work of a large number of AGVs becomes the main trend.The effective multi-agent path finding(MAPF) algorithm is urgently needed to ensure the efficiency and realizability of the whole system. The complex terrain of outdoor scenarios is fully considered by using different values of passage cost to quantify different terrain types. The objective of the MAPF problem is to minimize the cost of passage while the Manhattan distance of paths and the time of passage are also evaluated for a comprehensive comparison. The pre-path-planning and real-time-conflict based greedy(PRG) algorithm is proposed as the solution. Simulation is conducted and the proposed PRG algorithm is compared with waiting-stop A^(*) and conflict based search(CBS) algorithms. Results show that the PRG algorithm outperforms the waiting-stop A^(*) algorithm in all three performance indicators,and it is more applicable than the CBS algorithm when a large number of AGVs are working collaboratively with frequent collisions.

Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms

The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.

Study on scheduling algorithm for multiple handling requests of single automated guided vehicles

To solve the problem of small amount of machining centers in small and medium flexible manufacture systems(FMS), a scheduling mode of single automated guided vehicle(AGV) is adopted to deal with multiple transport requests in this paper. Firstly, a workshop scheduling mechanism of AGV is analyzed and a mathematical model is established using Genetic Algorithm. According to several sets of transport priority of AGV, processes of FMS are encoded, and fitness function, selection, crossover, and variation methods are designed. The transport priority which has the least impact on scheduling results is determined based on the simulation analysis of Genetic Algorithm, and the makespan, the longest waiting time, and optimal route of the car are calculated. According to the actual processing situation of the workshop, feasibility of this method is verified successfully to provide an effective solution to the scheduling problem of single AGV.

Application of Automated Guided Vehicle (AGV) Based on Inductive Guidance for Newsprint Rolls Transportation System

The paper presents the development and performance of a kinematics control scheme for the AGV based on inductive guidance in transporting newsprint rolls. The required error is pre-computed using a kinematics model of the AGV taking into account the effect of various factors that contribute to improve tracking performance of the AGV. Simulation and experimental results illustrate that the kinematics model performs well and the results of various factors contribute to tracking performance of the AGV.

Development of IoT Based Mobile Robot for Automated Guided Vehicle Application

Mobile robot has been one of the researches focuses in this era due to the demands in automation.Many industry players have been using mobile robot in their industrial plant for the purpose of reducing manual labour as well as ensuring more efficient and systematic process.The mobile robot for industrial usage is typically called as Automated Guided Vehicle(AGV).The advances in the navigation technology allows the AGV to be used for many tasks such as for carrying load to pre-determined locations sent from mobile app,stock management and pallet handling.More recently,the concept of Industry 4.0 has been widely practiced in the industries,where important process data are exchange over the internet for an improved management.This paper will therefore discuss the development of Internet of Things(IoT)bases mobile robot for AGV application.In this project a mobile robot platform is designed and fabricated.The robot is controlled to navigate from one location to another using line following mechanism.Mobile App is designed to communicate with the robot through the Internet of Things(IoT).RFID tags are used to identify the locations predetermined by user.The results show that the prototype is able to follow line and go to any location that was preregistered from the App through the IoT.The mobile robot is also able to avoid collision and any obstacles that exist on its way to perform any task inside the workplace.

AGV Scheduling Optimization of Automated Port Based on Disruption Management

The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.

Prescribed-time stabilisation control of differential driven automated guided vehicle

The position control problem of differential-driven automated guided vehicles(AGVs)based on the prescribed-time control method is studied.First,an innovative orientation error function is proposed by an auxiliary arcsine function about the orientation angle.Thus,the problem of position control of AGV is transformed into the stabilisation control of the kinematic system.Second,by introducing a reserved time parameter and a smooth switching function,a novel time-varying scaling function is proposed.This novel scaling function avoids the risk of infinite gain in the conventional prescribed-time control method while ensuring the smoothness of control laws.Then,an improved velocity constraint function is proposed using the Gaussian function.Compared with the existing constraint function,the proposed method not only has more smoothness but also solves the balance point errors caused by the large AGV orientation errors.The presented method ensures that the AGV reaches the target position in a prescribed time.Hence,the upper bound of the AGV system state can be determined by adjusting parameters.Matlab simulation results show that the proposed controller can effectively make the AGV system state satisfy the prescribed bound.

基于深度强化学习的多自动导引车运动规划

为解决移动机器人仓储系统中的多自动导引车(AGV)无冲突运动规划问题,建立了Markov决策过程模型,提出一种新的基于深度Q网络(DQN)的求解方法。将AGV的位置作为输入信息,利用DQN估计该状态下采取每个动作所能获得的最大期望累计奖励,并采用经典的深度Q学习算法进行训练。算例计算结果表明,该方法可以有效克服AGV车队在运动中的碰撞问题,使AGV车队能够在无冲突的情况下完成货架搬运任务。与已有启发式算法相比,该方法求得的AGV运动规划方案所需要的平均最大完工时间更短。

基于改进蚁群算法的AGV路径规划研究

针对传统蚁群算法在自动导引车(automated guided vehicle,AGV)路径规划研究中存在收敛速度慢、搜索能力差以及容易陷入局部最优等问题,文章提出一种改进蚁群算法。引入自适应启发式函数,增加蚁群寻优方向性;改进信息素更新策略,避免陷入局部最优解;动态调整信息素挥发系数,使其随着迭代时期而减小,从而提高算法搜索效率、加快算法收敛速度。仿真实验结果表明,相较于其他算法,在相同环境下文章所提改进蚁群算法具有较好的收敛性和较高的寻优能力。

低碳自动化码头AGV物流调度和有序充电的联合经济优化

针对现有自动化码头自动导航集卡(AGV)充电策略未能与分布式电源出力充分协同等问题,提出一种自动化码头AGV物流调度和有序充电联合经济优化方法.首先,解析AGV物流调度和充电调度之间的协同关系,构建联合优化框架;其次,针对AGV物流调度模块,提出考虑码头内外集卡道路隔离要求的AGV行驶距离计算方法;再次,针对AGV充电模块,定义AGV充电状态的判断条件和选桩方法;进而以码头购电费用最小为目标,考虑分时电价、风光上网电价、电量平衡约束、终止时刻的剩余电量约束、决策变量的上下限约束、物流调度约束,构建物流调度和有序充电联合优化模型,提出基于改进粒子群优化算法的快速求解方法;最后,通过某码头的实际案例,验证所提方法的有效性和经济性.

融合NSGA-II和CSA的多目标车间调度

针对在灵活车间系统中调度作业和自动引导车(automated guide vehicle,AGV)的同时调度问题,考虑在有限多个AGV和加工机台的情况下,以最小化最大完工时间、单个AGV搬运消耗时间及所有AGV搬运总消耗时间为目标函数,设计融合NSGA-II(non-dominated sorting genetic algorithms)和克隆选择(clonal selection algorithm,CSA)的改进算法INGCSA来解决此类问题。采用工件、加工机台和AGV三部分编码;引入非支配排序和目标函数值大小排序后总得分进行种群分层,从而有效地保留优秀个体;针对克隆后的种群,对不同等级的种群采取不同的变异概率,并对染色体进行内部交换与均匀交叉混合交换的基因重组,有效地提高了种群的多样性与防止陷入局部最优。通过三组对比实验,验证了该算法在探索最优解时,具有运行时间短、稳定性高和收敛性好等优点。

基于D3QN-PER的集装箱码头IGV任务动态分配研究

针对自动化集装箱码头智能导引车(IGV)作业环境动态、作业过程受电池电量约束等问题,研究不确定环境下考虑充电的IGV任务动态分配方法。分析不同载重下IGV充放电过程,提出基于电量区间自主决策的充电策略。为规避动态环境下IGV作业时间不确定对装卸效率的影响,以IGV和集装箱任务信息为状态空间,以集装箱分配规则为备选动作,综合装卸等待成本、IGV搬运成本和充电成本设计即时奖励,基于深度强化学习构建面向IGV任务分配的马尔可夫模型。为克服传统深度Q网络(DQN)过估计、收敛困难和训练不稳定问题,设计集成优先级经验回放(PER)机制和决斗双重深度Q网络(D3QN)结构的D3QN-PER算法。实验结果表明:D3QN-PER算法比单一规则决策和随机决策的IGV搬运任务总完工时间平均分别改进4.60%和12.05%,具有更好的收敛性能和训练稳定性。

基于改进A^(*)算法的AGV全局路径规划

采用八邻域搜索策略的传统A^(*)算法对AGV(自动导引车)进行全局路径规划时,存在搜索邻域过多、实时性差和生成路径拐点多等问题,本研究采用三邻域与八邻域混合搜索策略对传统A^(*)算法的搜索策略进行改进,改进后的A^(*)算法在当前搜索点周围不存在障碍物时,选取指向终点的三个栅格作为搜索邻域,当搜索点周围出现障碍物,则转换为传统的八邻域搜索,并在完成搜索后,对搜索路径进行拉直处理,消除多余拐点,减少路径长度。仿真实验结果表明,改进A^(*)搜索算法能有效缩短搜索时间、减少路径拐点数量并缩短路径长度,提高AGV运行效率。

基于人工势场算法改进的AGV自主避障路径规划算法及其应用

为解决AGV在瓦楞纸包装车间运行中难于从起始到目标位置寻求最优且避障的路径难题,提出了一种基于人工势场算法改进的自主避障路径规划算法。首先,将目的地与AGV的相对距离加入斥力函数中,对引力函数建立梯度,以解决目的地与障碍物较近导致的目标不可达问题;其次,在局部极小值附近增加虚拟障碍物以助力AGV逃离局部极小值区域;最后,借助二阶贝塞尔曲线对搜索到的路径实施平滑处理,确保AGV的路径可行性。仿真实验表明,改进后的人工势场算法应用于包装车间环境下AGV避障路径规划是可行且高效的。

AGV“货到人”拣选系统的移动货架选择方法

基于自动导引车(AGV)的“货到人”拣选系统,各品类商品可以拆零存放在不同货架上,同一货架也可以存储多种商品,货架的位置也是随机移动的。面对待拣选的订单,选择哪些货架移动到拣选台,以满足订单的品类和数量要求,并最小化选取移动货架个数,是此类型仓库需要解决的关键问题。针对该问题,文章提出基于订单相似度进行订单分批,再通过线性递减权重的粒子群算法来优化移动货架选择解。由于移动货架的优化问题是NP-hard问题,线性规划求解器难以求解,实验结果表明,与线性规划求解器Lingo相比,文本的两阶段移动货架选择方法是有效的,可以在短时间得出求解方案,大大提升了求解效率。敏感性分析进一步揭示了批次数量、品类数量、货架数量以及订单数量,对搬运货架次数的影响,为管理者提供了决策依据。

应用于自动导引小车无线充电系统的导航与供电一体化线圈研究

在自动导引小车(AGV)中采用无线电能传输(WPT)可以提高AGV充电及搬运效率。相比于电动汽车,AGV的导航系统为WPT的应用增加了新的挑战。为降低系统的复杂性和成本,该文提出一种无线充电线圈结构:DAD线圈,该线圈既可以实现无线供电,又可以实现偏移检测。首先,结合DAD线圈的气隙磁场分布,对比方形线圈结构,分析DAD线圈导航和供电的原理;然后,对采用LCC-S型补偿拓扑的DAD线圈进行建模分析,提出具有耦合机构输出电压不受负载影响的谐振元件参数配置条件,进而给出耦合机构的电压增益、输出功率与传输效率表达式;最后,为验证所提出的DAD耦合机构偏移检测性能和系统传输特性,搭建30 mm间距的400 W无线充电样机装置,对DAD线圈的导航性能与供电性能进行了验证,在偏移范围为(-50~+50) mm之间,DAD线圈可以指示偏移方向与偏移大小,且效率波动在5%范围内,可以实现稳定的电能传输。

不确定情景下AGV系统调度算法的仿真平台

不确定情境下的完整自动引导车(automated guided vehicle,AGV)系统调度算法,除了要包含常见的任务指派策略和路径规划算法之外,还需要包含冲突应对策略。由于存在随机性,这样的调度算法难以从理论上分析其特性,相关研究也较少。基于离散事件仿真技术,设计并开发了一个AGV系统仿真平台,可以灵活地设置调度问题,并选择调度算法中的任务指派策略、路径规划算法和路径冲突应对策略进行仿真。该平台具有可视化的界面,可以直观地观察AGV的运行状态和调度算法的表现,也可以输出最终的仿真实验统计数据。实验表明:该平台可以准确有效地对不确定情景下的调度算法进行评估与比较。

自动分拣仓库中多载量AGV调度与路径规划算法

在自动分拣仓库中,多载量自动导引小车(AGV)具有强运输能力,但其多载量特征也增加了调度与路径规划的复杂性。针对多载量AGV调度与路径规划的协同优化问题,以最小化最大搬运完成时间为目标,建立了该问题的混合整数线性规划模型,并提出一种聚类协同优化算法。算法首先定义了包裹相似度,设计聚类算法划分包裹组,使每个包裹组可由多载量AGV在一次作业中完成分拣;进而针对问题的多决策特征,设计协同进化遗传算法对包裹组进行指派和排序,并将无冲突路径规划算法引入到协同进化遗传算法的解码方案中,用以搜索最优路径并解决多AGV路径冲突,从而实现了多载量AGV调度与路径规划的协同优化。通过不同问题规模的仿真实验验证了所提算法的高效性和稳定性。

基于势博弈的自动化码头路网流量均衡研究

为提高自动化集装箱码头(自动化码头)路网利用率和均衡性,本文基于自动化码头各子路径的拥挤程度、自动导向车(AGV)的运输速度以及各子路径的选择概率等因素,将分布式的AGV流量调度问题转化为AGV之间的博弈模型,提出一种基于势博弈理论的AGV流量调度模型;设计分布式路径选择算法帮助AGV更新策略;系统信息更新算法用于更新道路信息并选择更新策略的AGV;通过这两种算法求解AGV的均衡调度策略。结合3艘实际船舶任务,进行数值仿真实验。仿真结果表明:分布式路径选择方法相较于传统路径选择方式具有更优性,船舶任务量、AGV数量及速度是影响AGV作业效益的关键因素,当AGV的速度为6 m·s^(-1)时,船舶的总收益最大。本文可以为码头管理者确定AGV调度策略提供决策支持。