An Effective Optimization Method for Integrated Scheduling of Multiple Automated Guided Vehicle Problems

Automated Guided Vehicle(AGV)scheduling problem is an emerging research topic in the recent literature.This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs.To reduce the transportation cost of AGVs,this work also proposes an optimization method consisting of the total running distance,total delay time,and machine loss cost of AGVs.A mathematical model is formulated for the problem at hand,along with an improved Discrete Invasive Weed Optimization algorithm(DIWO).In the proposed DIWO algorithm,an insertion-based local search operator is developed to improve the local search ability of the algorithm.A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning.Comprehensive experiments are conducted,and 100 instances from actual factories have proven the effectiveness of the optimization method.

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.

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.

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。

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。

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.

分布式AGV调度研究综述与发展趋势分析

智能物流是智能制造的重要组成部分,也是物流行业发展的必然趋势。自动导引车(automated guided vehicle,AGV)可无缝衔接企业生产系统和仓储系统,是实现智能物流的一种重要技术装备。AGV调度对于提高物流效率、降低能源消耗、减少运输过程中货物的损坏和事故的发生等至关重要。AGV调度问题涉及任务分配和路径规划两个子问题,从调度方式上可分为集中式调度和分布式调度。随着AGV设备智能化水平的提升,分布式AGV系统凭借可扩展性、高可靠性等优势已成为当前研究的热点。首先描述了分布式AGV调度问题;然后分别阐述分析了分布式AGV任务分配和路径规划领域的研究现状;最后对分布式AGV调度未来的发展前景进行了展望。

基于快速平稳幂次趋近律AGV滑模轨迹跟踪控制研究

针对应用于地下车库的自动导引车(automated guided vehicle,AGV)轮式泊车机器人轨迹跟踪过程中的位置偏差和姿态偏差问题,提出了一种快速平稳幂次趋近律的滑模轨迹跟踪控制方法。首先,在泊车机器人坐标系下建立运动学模型,通过全局坐标系与机器人坐标系之间的转换关系,得到泊车机器人在全局坐标系下的运动学模型;然后采用Lyapunov直接法设计滑模切换函数,采取快速平稳幂次趋近律的方法使泊车AGV从偏差状态快速到达滑模切换面,使AGV泊车机器人快速平稳地跟踪给定参考轨迹。最后,通过MATLAB进行仿真试验,仿真结果验证了所提方法的可行性与有效性。

考虑AGV运输和机器转速的作业车间绿色调度研究

在车间实际生产中,机器在加工状态和空载状态下都需要消耗能量,且机器在不同转速下的能耗也不同。此外,随着自动导引车(AGV)在车间的广泛应用,其能耗也不可忽略,因此对考虑AGV和机器转速的作业车间绿色调度问题进行研究具有重要意义。完工时间不仅被认为是表示车间生产效率的指标,而且在一定程度上意味着机器空载能耗的减少、机器利用率的提高。为此,对考虑AGV运输和机器转速的作业车间完工时间和能耗优化问题进行了研究。首先,对作业车间机器的能耗、AGV能耗进行了分析,探讨了AGV、工件和机器之间的工艺约束,建立了考虑AGV运输和机器转速的作业车间绿色调度模型;然后,采用两阶段优化的方式分别对完工时间和车间总能耗进行了优化,在第一阶段,机器以最高转速进行了加工,采用改进麻雀搜索算法对其最大完工时间进行了优化;在第二阶段,在保证第一阶段优化得到的完工时间最小值不变的前提下,提出了一种间隙档位调整策略,通过降低部分工序的机器转速来降低车间能源消耗,实现对车间总能耗的优化;最后,采用标准用例对算法进行了仿真实验。研究结果表明:在第一阶段采用改进麻雀搜索算法,提高了种群质量,加快了算法的收敛速度,算法具有较强的跳出局部最优能力;第二阶段在保证完工时间的基础上,采取间隙档位调整策略来降低能耗,通过10个用例测试表明能耗可减少1.13%~5.18%。该结果验证了考虑AGV和机器转速绿色调度模型的正确性以及改进麻雀算法的有效性。

基于ROS架构的叉车型AGV控制系统设计开发研究

针对叉车型AGV开发成本高、系统封闭、可拓展性差的问题,该文基于开源机器人操作系统(ROS),设计了一套新的控制系统。该控制系统采用双层控制架构,通过模块化的设计思想实现了控制平台的搭建。其中,上层控制器采用装有Linux操作系统和ROS的嵌入式工控机实现叉车型AGV的即时定位与建图(SLAM)及路径跟踪功能;下层控制器采用西门子PLC实现对I/O信号的采集,以及通过CAN网络实现对舵轮的行走与转向控制及信息采集、对电源系统实现监控、对货叉起升高度检测,同时可以与车间调度计算机通过无线网络进行AGV调度与在线监控。

基于改进A^(*)算法与时间窗检测法的多AGV路径规划方法

为了能够让多个自动导引车(AGV)在复杂环境中实现高效和安全的路径规划,提出一种基于改进A^(*)算法和时间窗检测法相结合的方法。采用改进A^(*)算法为每个AGV生成一条初始路径,再利用时间窗检测法对每个AGV进行冲突检测和解决,得到一组可行且无冲突的路径。通过python进行仿真试验,验证了所提方法的有效性,并与其他方法进行对比分析,结果表明,所提方法在解决多AGV协同路径规划问题上有一定优越性,能够让多个AGV更好地协同工作,实现高效和安全的路径规划。

强化学习下浅充浅放充电策略AGV调度研究

针对自动化集装箱码头自动导引车(AGV)调度中的充电问题,考虑浅充浅放充电策略构建了混合整数优化模型。该模型以最小化AGV最终完工时间为目标,在考虑AGV电池电量变化以及AGV不同状态耗电差异的约束下,利用AGV空闲时间和一个作业循环结束时间补电,减少AGV充电次数,进而减少总完成时间。模型采用Wolf-PHC强化学习进行求解,并分别与GAMS求解器、Q-learning算法及遗传算法(genetic algorithm,GA)求解结果进行比较,以验证模型的有效性和算法的优越性。算例分析表明在浅充浅放充电策略下AGV利用效率较高,且Wolf-PHC与GA的结合对模型求解效果更佳。

多目标优化算法在AGV调度的应用研究

针对多品种小批量生产的车间AGV调度问题,以客户满意度最大化和配送总成本最小化为优化目标,提出了一种有效的多目标优化算法。首先建立了多目标混合整数线性规划模型,在此基础上提出了多目标进化算法。该算法在种群初始化引入了启发式算法,采用基于理想点的多目标局部搜索增强挖掘能力,在非支配解的有价值信息的基础上设计了两点交叉运算方法,通过重启策略避免了所提算法陷入局部最优。最后通过某企业的调度实例进行了对比实验,结果表明,所提算法求解该问题的性能明显优于现有的多目标算法。

跳点搜索融合双向并行蚁群算法的AGV路径规划研究

在静态栅格地图中,针对传统蚁群算法进行AGV(Automated Guided Vehicle,自动引导车)路径规划收敛慢且搜索结果容易陷入局部最优的问题,提出一种融合跳点搜索(Jump Point Search,JPS)和双向并行蚁群搜索的改进算法.首先,对实际研究环境进行栅格化建模,使用改进的跳点搜索算法生成双向搜索的初始次优路径,为双向蚁群搜索提供初始搜索方向参考.其次,在双向并行蚁群搜索过程中采用改进的转移概率启发函数,该函数在确定下一个转移节点时考虑了避免AGV与障碍物碰撞的因素,同时通过设计信息素共享机制并结合改进的信息素增量及浓度两种融合模型,共享和更新全局信息素浓度,以更好地探索和优化路径,保证双向路径连结.最后,与传统蚁群算法进行实验结果对比,验证了改进算法的全局搜索能力、效率和安全性.

基于预瞄LMPC的港口AGV路径跟踪控制

路径跟踪控制是港口自动导引车(Automated Guided Vehicle,AGV)的一项关键技术,现有路径跟踪控制方法存在难以在U型弯道等具有较大幅度曲率突变的参考路径上保持较高精确性以及实时性较差等问题。为此以无预瞄线性模型预测控制(Linear Model Predictive Control,LMPC)为基础,将传统展开点改为AGV前方的预瞄展开点,提出了基于预瞄LMPC的港口AGV路径跟踪控制方法,并通过联合仿真将其与非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)、无预瞄LMPC进行了对比。仿真结果显示,预瞄LMPC精确性较高,位移误差不超过0.0602 m,精确性与NMPC相当,显著优于无预瞄LMPC,相同工况下该指标减小幅度可达93.25%;预瞄LMPC的实时性较好,每个控制周期内解算时间均不超过6.0203 ms,实时性与无预瞄LMPC相当,显著优于NMPC,相同工况下该指标减小幅度可达72.52%。