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。

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.

Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

This paper investigates the problem of path tracking control for autonomous ground vehicles(AGVs),where the input saturation,system nonlinearities and uncertainties are considered.Firstly,the nonlinear path tracking system is formulated as a linear parameter varying(LPV)model where the variation of vehicle velocity is taken into account.Secondly,considering the noise effects on the measurement of lateral offset and heading angle,an observer-based control strategy is proposed,and by analyzing the frequency domain characteristics of the derivative of desired heading angle,a finite frequency H_∞index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error.Thirdly,sufficient conditions are derived to guarantee robust H_∞performance of the path tracking system,and the calculation of observer and controller gains is converted into solving a convex optimization problem.Finally,simulation examples verify the advantages of the control method proposed in this paper.

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.

基于MATLAB的AGV运行故障仿真研究

自动导引车(AGV)是自动化物流技术的三大标志之一。介绍了AGV及其发展现状,提出了一种基于MATLAB语言的AGV运行故障仿真思想,并结合具体实例进行仿真,为AGV的选型提供了一种参考思路。

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.

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%。

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.

An Ellipsoidal Set-Membership Approach to Distributed Joint State and Sensor Fault Estimation of Autonomous Ground Vehicles

This paper is concerned with the problem of distributed joint state and sensor fault estimation for autonomous ground vehicles subject to unknown-but-bounded(UBB)external disturbance and measurement noise.In order to improve the estimation reliability and performance in cases of poor data collection and potential communication interruption,a multisensor network configuration is presented to cooperatively measure the vehicular yaw rate,and further compute local state and fault estimates.Toward this aim,an augmented descriptor vehicle model is first established,where the unknown sensor fault is modeled as an auxiliary state of the system model.Then,a new distributed ellipsoidal set-membership estimation approach is developed so as to construct an optimized bounding ellipsoidal set which guarantees to contain the vehicle’s true state and the sensor fault at each time step despite the existence of UBB disturbance and measurement noises.Furthermore,a convex optimization algorithm is put forward such that the gain matrix of each distributed estimator can be recursively obtained.Finally,simulation results are provided to validate the effectiveness of the proposed approach.

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.

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.

Enhancing safety and efficiency in automated container terminals: Route planning for hazardous material AGV using LSTM neural network and Deep Q-Network

As the proliferation and development of automated container terminal continue,the issues of efficiency and safety become increasingly significant.The container yard is one of the most crucial cargo distribution centers in a terminal.Automated Guided Vehicles(AGVs)that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials,while also maximizing efficiency,is a complex challenge.This research introduces an algorithm that integrates Long Short-Term Memory(LSTM)neural network with reinforcement learning techniques,specifically Deep Q-Network(DQN),for routing an AGV carrying hazardous materials within a container yard.The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials.Utilizing real data from the Meishan Port in Ningbo,Zhejiang,China,the actual yard is first abstracted into an undirected graph.Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored,a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials,which are incorporated into the map as background AGVs.Subsequently,DQN is employed to plan the route for an AGV transporting hazardous materials,aiming to reach its destination swiftly while avoiding encounters with other AGVs.Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs.Compared to the method where hazardous material AGV follow the shortest path to their destination,the avoidance efficiency was enhanced by 3.11%.This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals.Additionally,it provides insights for designing avoidance schemes for autonomous driving AGVs,offering solutions for complex operational environments where safety and efficient navigation are paramount.

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。

A Rapid Path Planner for Autonomous Ground Vehicle Using Section Collision Detection

Rapid path planner plays an important role in autonomous ground vehicle （AGV） operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree （RRT） can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 mx 170 m area with moderate obstacle complexity.

Multimodal processes optimization subject to fuzzy operation time constraints:declarative modeling approach

We present an extension of the resource-constrained multi-product scheduling problem for an automated guided vehicle(AGV) served flow shop, where multiple material handling transport modes provide movement of work pieces between machining centers in the multimodal transportation network(MTN). The multimodal processes behind the multi-product production flow executed in an MTN can be seen as processes realized by using various local periodically functioning processes. The considered network of repetitively acting local transportation modes encompassing MTN's structure provides a framework for multimodal processes scheduling treated in terms of optimization of the AGVs fleet scheduling problem subject to fuzzy operation time constraints. In the considered case, both production takt and operation execution time are described by imprecise data. The aim of the paper is to present a constraint propagation(CP) driven approach to multi-robot task allocation providing a prompt service to a set of routine queries stated in both direct and reverse way. Illustrative examples taking into account an uncertain specification of robots and workers operation time are provided.