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.

Global path guided vehicle obstacle avoidance path planning with artificial potential field method

An artificial potential field method based on global path guidance(G-APF)is proposed for target unreachability and local minima problems of the conventional artificial potential field(APF)method in complex environments with dynamic obstacles.First,for the target unreachability problem,the global path attraction is added to the APF;second,an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem;finally,based on the obstacle detection optimisation method,the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision-free.Experiments show that the method optimises 40.8%of the total path corners,reduces 81.8%of the number of path oscillations,and shortens 4.3%of the path length in Map 1.It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.

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.

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。

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%.

Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

Research on AGV task path planning based on improved A^(*) algorithm

Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.

Task Offloading Optimization for AGVs with Fixed Routes in Industrial IoT Environment

In order to solve the delay requirements of computing intensive tasks in industrial Internet of things,edge computing is moving from theoretical research to practical applications.Edge servers(ESs)have been deployed in factories,and on-site auto guided vehicles(AGVs),besides doing their regular transportation tasks,can partly act as mobile collectors and distributors of computing data and tasks.Since AGVs may offload tasks to the same ES if they have overlapping path segments,resource allocation conflicts are inevitable.In this paper,we study the problem of efficient task offloading from AGVs to ESs,along their fixed trajectories.We propose a multi-AGV task offloading optimization algorithm(MATO),which first uses the weighted polling algorithm to preliminarily allocate tasks for individual AGVs based on load balancing,and then uses the Deep Q-Network(DQN)model to obtain the updated offloading strategy for the AGV group.The simulation results show that,compared with the existing methods,the proposed MATO algorithm can significantly reduce the maximum completion time of tasks and be stable under various parameter settings.

考虑设备故障和电量管理的自动导引车调度方法

Intermediate charging and sudden failure of automatic guided vehicles(AGVs)interrupt and severely affect the stability and efficiency of scheduling.Therefore,an AGV scheduling approach considering equipment failure and power management is proposed for outfitting warehouses.First,a power consumption model is established for AGVs performing transportation tasks.The powers for departure and task consumption are used to calculate the AGV charging and return times.Second,an optimization model for AGV scheduling is established to minimize the total transportation time.Different conditions are defined for the overhaul and minor repair of AGVs,and a scheduling strategy for responding to sudden failure is proposed.Finally,an algorithm is developed to solve the optimization model for a case study.The method can be used to plan the charging time and perform rescheduling under sudden failure to improve the robustness and dynamic response capability of AGVs.

自适应伸缩因子的变论域模糊控制的AGV纠偏

针对自动导航车(Automated Guided Vehicle, AGV)纠偏控制响应较慢这一问题,提出了一种自适应伸缩因子的变论域模糊纠偏控制方法。建立了AGV的运动学方程,以车体位置、角度偏差为输入量,以车体转向角度为输出量,在设计模糊纠偏控制器的基础上,引入了基于函数模型的参数自适应伸缩因子,将系统误差作为输入,将伸缩因子参数作为输出,实现了伸缩因子基于函数模型的参数实时自适应调整,使模糊变论域跟随误差变化而调整,避免因变论域不恰当导致控制效果变差的问题。最后通过MATLAB环境下的Simulink仿真研究,将该控制器的纠偏控制效果与传统模糊控制、传统变论域模糊控制的纠偏控制效果进行仿真对比,验证了该控制器的纠偏控制响应更快。

基于启发式强化学习的AGV路径规划

针对传统算法、智能算法与强化学习算法在自动引导小车(automated guided vehicle,AGV)路径规划中收敛速度慢、学习效率低的问题,提出一种启发式强化学习算法,并针对传统Q(λ)算法,设计启发式奖励函数和启发式动作选择策略,以此强化智能体对优质行为的探索,提高算法学习效率.通过仿真对比实验,验证了基于改进Q(λ)启发式强化学习算法在探索次数、规划时间、路径长度与路径转角上都具有一定的优势.

移动机器人智能车场停车资源调度分配

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。

基于视觉导航仓储AGV的模糊PID控制

研究了仓储自动导引运输车(automatic guided vehicle,AGV)运动过程中由于随机非线性误差引起的AGV行走左右位置偏差问题。以建立AGV底盘相对应的运动学模型为基础,在仓储AGV运动控制中采用了模糊PID方法,依靠视觉传感器反馈的偏移误差和偏移误差变化率,通过仓储AGV的合成速度进行运动调节。通过仿真实验发现在AGV运动控制中应用模糊PID控制方法较传统PID控制方法的结果曲线误差小。经测试,在视觉导航仓储AGV中运用模糊PID控制方法较传统PID控制方法的工作效率提高了5%。

A Deadlock-Avoidance Dispatching Method for Multiple-Load AGVs Based Transportation System

In recent years,multiple-load automatic guided vehicle(AGV)is increasingly used in the logistics transportation fields,owing to the advantages of smaller fleet size and fewer occurrences of traffic congestion.However,one main challenge lies in the deadlock-avoidance for the dispatching process of a multiple-load AGV system.To prevent the system from falling into a deadlock,a strategy of keeping the number of jobs in the system(NJIS)at a low level is adopted in most existing literatures.It is noteworthy that a low-level NJIS will make the processing machine easier to be starved,thereby reducing the system efficiency unavoidably.The motivation of the paper is to develop a deadlock-avoidance dispatching method for a multiple-load AGV system operating at a high NJIS level.Firstly,the deadlock-avoidance dispatching method is devised by incorporating a deadlock-avoidance strategy into a dispatching procedure that contains four sub-problems.In this strategy,critical tasks are recognized according to the status of workstation buffers,and then temporarily forbidden to avoid potential deadlocks.Secondly,three multiattribute dispatching rules are designed for system efficiency,where both the traveling distance and the buffer status are taken into account.Finally,a simulation system is developed to evaluate the performance of the proposed deadlock-avoidance strategy and dispatching rules at different NJIS levels.The experimental results demonstrate that our deadlock-avoidance dispatching method can improve the system efficiency at a high NJIS level and the adaptability to various system settings,while still avoiding potential deadlocks.

Analysis of Multi-AGVs Management System and Key Issues: A Review

Multiple Automatic Guided Vehicle(multi-AGVs)management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario,such as flexible manufacturing systems,warehouses,container terminals,etc.This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator.The multi-AGVs management problem is a multi-objective,multi-constraint combinatorial optimization problem,which depends on the types of application scenarios.This article classifies and compares the research papers on multi-AGVs management in detail.Firstly,according to the different dimensions of the problem,the multi-AGVs management system is analyzed from three perspectives,namely,1)task dimensiondispatch,2)spatial dimension-path,and 3)time dimension-scheduling.The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well.Secondly,according to their utility,the multi-AGVs management problems are divided into three categories:1)cost reduction,resource-oriented,2)efficiency improvement,problem-oriented,3)personalized demand,goal-oriented.The algorithm and methods of the different utility-oriented are analyzed and discussed.The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems.Finally,according to the literature review,suggestions are made for further research.

Mobileman,Construction Agile Goods Delivery Robot

The traditional automated guided vehicle(AGV) on goods delivery faces the challenges when task space expands beyond 2 D plans. 3 D environments such as uneven terrain, ramps, and staircase are typical in construction site. Thus, the key to introducing this technology into construction industry is to improve AGV’s stability and autonomous navigation ability in more complex three-dimensional environments. In this paper, mobileman, a novel tracked autonomous guide vehicle, is introduced. Compared with other construction robots, mobileman maximizes its load capacity on the basis of assuring accessibility. Furthermore, its modular designs and self-balancing platform enable it to cope with more complex challenging scenarios, such as staircase with 35-degree sloped staircase, while another modular design featured automated loading and unloading functionality. The mobile base specifications were presented in section two, and modular designs and exploration of the navigation system on construction site were illustrated in the rest of sections.