Multiple Automated Guided Vehicles Research Articles

Conflict-free coordination planning for multiple automated guided vehicles in an intelligent warehousing system

With the rapid development of intelligent manufacturing, the application of automated guided vehicles (AGVs) in intelligent warehousing systems has become increasingly common. Efficiently planning the conflict-free paths of multiple AGVs while minimizing the total task completion time is crucial for evaluating the performance of the system. Distinguishing itself from recent approaches where conflict avoidance strategy and path planning algorithm are executed independently or separately, this paper proposes an improved conflict-free A* algorithm by integrating the conflict avoidance strategy into the initial path planning process. Based on the heuristic A* algorithm, we use the instruction time consumption as the key evaluation indicator of the cost function and add the turning consumption in the future path cost evaluation. Moreover, the expansion mode of child nodes is optimized where a five-element search set containing the “zero movement” is proposed to implement a proactive pause-wait strategy. Then the prediction rules are designed to add constraints to three types of instructions based on the timeline map, guiding the heuristic planning to search for conflict-free child nodes. Extensive simulations show that the coordination planning based on the improved conflict-free A* algorithm not only effectively achieves advanced conflict avoidance at the algorithmic level, but also exhibits lower computational complexity and higher task completion efficiency compared to other coordination planning methods.

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework’s effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics.

Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques

In recent years, automated guided vehicles (AGVs) have been widely adopted to automate material handling procedures in manufacturing shopfloors and distribution centers. AGV scheduling is the procedure of allocating a transportation task to an AGV, which has large impacts on the efficiency of an AGV system with multiple AGVs. In order to optimize the operations of multi-AGV systems, AGV scheduling decisions should be made with consideration of practical issues such as buffer space limitations and battery charging. However, previous studies have often overlooked those issues. To fill this gap, this paper proposes a simulation-based multi-AGV scheduling procedure for practical shopfloors with limited buffer capacity and battery charging. Furthermore, we propose three kinds of rules: job selection rules, AGV selection rules, and charging station selection rules, for AGV scheduling in practical shopfloors. The performance of the rules is evaluated through multi-scenario simulation experiments. The FlexSim software v.2022 is used to develop a simulation model for the experiments, and the experimental findings indicate that the job selection rules have larger impacts on the average waiting time than the other kinds of rules.

Actuator fault-tolerant control allocation for cooperative transportation of multiple omnidirectional mobile robots

In this paper, we propose a fault detection, fault isolation, and control reconfiguration (FDIR) for multiple automated guided vehicles (AGVs) to accomplish object transportation under actuator failures. Firstly, the observers are designed for the AGVs to detect any occurring faults in actuators. Secondly, banks of observers are employed to isolate the broken actuators. Finally, according to the isolation results, the controllers are reconfigured to accommodate the faults. The stability of the proposed methods is verified using the Lyapunov technique. It is worth noting that the proposed FDIR method considers the uncertainties in system dynamics, enhancing its practicality and convenience for implementation. The effectiveness of the proposed algorithm is demonstrated via both numerical and experimental examples involving multiple omnidirectional AGVs experiencing actuator faults.

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

PurposeThis study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.Design/methodology/approachIn this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.FindingsThe experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.Originality/valueIn this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

A Machine Learning-Based Approach for Multi-AGV Dispatching at Automated Container Terminals

The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.

Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning

This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, with modifications made to the action and state space to fit the setting of AGV activities. While previous studies overlooked the energy efficiency of AGVs, this paper develops a well-designed reward function that helps to optimize the overall energy consumption required to fulfill all tasks. Moreover, we incorporate the e-greedy exploration strategy into the proposed algorithm to balance exploration and exploitation during training, which helps it converge faster and achieve better performance. The proposed MARL algorithm is equipped with carefully selected parameters that aid in avoiding obstacles, speeding up path planning, and achieving minimal energy consumption. To demonstrate the effectiveness of the proposed algorithm, three types of numerical experiments including the ϵ-greedy MADDPG, MADDPG, and Q-Learning methods were conducted. The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency.

Scheduling the tasks of multiple AGVs in a fault-tolerant control way

The paper concerns a development of an approach for implementing fault-tolerant control (FTC) of automated guided vehicles (AGVs). The main challenge considered in the article is the way of taking into account in the scheduling algorithm the delays occurring in the system resulting from the use of transport means. Mostly, multiple AGVs are designed and implemented as a firm real-time system in which delays are treated as faults. The proposed framework allows to compensate the faults in the real time. In the paper a multiple AGVs that transport item from an output of a production system to a high bay warehouse is presented. The article presents the non-deterministic nature of a multi-AGV system and includes a mathematical description of the system using (max, +) algebra. Having a analytical description of AGVs system, a new FTC strategy is proposed. Moreover, in the paper an illustrative case study showing the performance of the developed approach is presented.

Two-tier MPC architecture for AGVs navigation assisted by edge computing in an industrial scenario

The next generation of mobile networks may be a bridge to move industrial control to edge computing. Embedded controllers physically installed in the industrial process can be replaced by software remotely executed as a service. In this context, Automated-Guided Vehicles (AGVs) are industrial agents that can benefit from this scenario by offloading computing-intensive tasks remotely to the edge. Additionally, an AGV control system based on remote software promotes flexibility, an essential requirement for Industry 4.0. Considering the remote control of AGV, the mobile robot may traverse areas where the signal is degraded, increasing the risks of collisions and accidents due to connection failures. Previous works suggest the adoption of Model Predictive Control (MPC) to control mobile robots in the occurrence of delays and packet losses. In this article, we propose a two-tier architecture of MPCs, one executed at the edge to plan the trajectory of multiple AGVs globally and the other executed individually in each AGV to keep it on the planned track. In simulations performed in an edge computing environment using a robot simulator, the vehicles follow the planned trajectory even with network degradation. In our simulated scenarios with delays and packet loss, predicted actions planned by the MPC on the edge avoided collisions between the vehicles.

Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model’s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.

Energy-efficient open-shop scheduling with multiple automated guided vehicles and deteriorating jobs

There is growing interest in energy-efficient production scheduling research because of the increasing energy shortage. However, most existing studies along this line of research have not considered the energy consumed by automated guided vehicles (AGVs) used in modern smart factories for production scheduling purposes. In this paper, we study an energy-efficient open-shop scheduling problem with multiple AGVs and deteriorating jobs. A multi-objective model with four objectives is formulated, aiming to simultaneously minimise the maximum ending time of all AGVs, the total idle time of machines and AGVs, the total tardiness of jobs, and the total energy consumption of machines and AGVs. An improved population-based multi-objective differential evolution (IMODE) algorithm is developed to solve the problem. The IMODE makes use of a problem feature-based heuristic and a mean entropy method to enhance the diversity of its initial population. A novel grey entropy parallel analysis-based fitness evaluation mechanism with reference points is adopted to evaluate the candidate solutions. To improve the local search ability of IMODE, a multi-level local search strategy is used. In the experimental study, Taguchi analysis is employed to obtain the best parameter combination. The effects of the main components of IMODE are validated via comprehensive comparison experiments. Extensive experimental results show that the IMODE is preferable to other well-known multi-objective algorithms at solving the problem being considered.

A neural network based multi-state scheduling algorithm for multi-AGV system in FMS

Due to its numerous advantages in terms of flexibility, maintainability and construction cost, the Flexible Manufacturing System (FMS) has received increasing attention over the last few years. The application of multi automated guided vehicle (AGV) transportation system promotes the flexibility of the FMS, which also imposes higher requirements for AGV scheduling algorithms. This paper develops a novel multi-state scheduling algorithm (MSSA) for AGV that makes a good trade-off between AGV utilization rate and total processing make-span in the FMS. Compared with classic idle-scheduling strategy, MSSA schedules more AGVs and tasks in each calculation, making its optimization target closer to the global optimization target. A neural network based travel time prediction is applied to improve the performance of the proposed algorithm on time accuracy. Five factors are used as neural network inputs to express the impact of vehicle state, distance travelled, AGV trajectory and multiple AGV collisions on travel time. Simulation experiments indicate the proposed algorithm has advantages in terms of total processing make-span, AGV load rate, AGV utilization and task execution time. The proposed algorithm has been applied in an actual air conditioner production line.

Conflict-free scheduling of large-scale multi-load AGVs in material transportation network

The process of scheduling an automated guided vehicle (AGV) includes task scheduling, path planning, and traffic control management. The conflict-free scheduling of large-scale multi-load AGVs is a challenging problem in manufacturing logistics and transportation. To solve the problem of scheduling such AGVs in a network logistics system, this study proposes a method of task allocation based on adjacency combination and the shortest path principle. Three priority rules for the mobility of AGVs between nodes are designed, and a transportation strategy that combines single and two-way paths is proposed to reduce computational complexity. By combining with Dijkstra’s method, the authors develop a method to prevent deadlocks and collisions between multiple AGVs based on a timetable of reservations that hierarchically handles conflicts among nodes in multiple stages. Such constraints as AGV congestion or deadlock weaken the effectiveness of the shortest distance rule-based solution. Based on the above, a heuristic search method based on variable neighborhood search is further proposed to optimize the problem of multi-AGV task assignment, and a corresponding theorem is given to avoid the generation of unfeasible solutions by the neighborhood operators and improve the efficiency of the solution. The results of experiments show that the proposed method can adequately solve the problem of scheduling multiple AGVs in a large and dense network.

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) and Dijkstra’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.

MARL-Based Cooperative Multi-AGV Control in Warehouse Systems

Automated guided vehicles (AGVs) are essential components for the automation of fulfillment centers, a type of warehouse, where goods are stored on shelves and carried by AGVs. To increase the productivity in inventory management, a well-organized cooperative path control is required to transport goods to the designated picking stations. In this paper, we propose a QMIX-based scheme for the cooperative path control of multiple AGVs. Although QMIX is the one of popular cooperative multi-agent reinforcement learning algorithms, we find that QMIX alone was not enough to increase productivity in warehouse systems. So, we develop two novel techniques that can be used with QMIX: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequential action masking</i> that eliminates all the collision cases and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">additional local loss</i> that improves collaboration of individual AGVs. They help to encourage the AGVs to cooperate more for high productivity. By extensive simulations, we present the superiority of the proposed scheme on several layouts in fulfillment centers. The effect of cooperation among AGVs in the proposed scheme is verified through the comparison study with the existing algorithms. Additionally, we show the generalization performance by investigating the reusability of the model trained with the proposed scheme.

A bi-level cooperative operation approach for AGV based automated valet parking

In urban areas, the number of cars has increased significantly in recent years, resulting in frequent traffic congestion in parking lots. Automated valet parking (AVP) system based on automated guided vehicles (AGVs) can relieve human from parking and improve efficiency to a certain extent due to their fully automatic control and operation. However, with the expansion of the scale of the whole parking lot, the current AGV based AVP system is facing the disadvantage of long-time queue congestion and even deadlock. In this paper, we systematically consider the traffic congestion faced by the AGV based AVP system and introduce a bi-level cooperative operation approach. The global cooperative parking space allocation is considered in the upper-level, and the cooperative driving of multiple AGVs in the conflict zone is resolved in the lower-level. The upper-level problem is formulated as a Markov decision process, and a global cooperative allocation method is obtained by using deep reinforcement learning (DRL). In the lower-level, with the modified planning based cooperative driving method, multiple AGVs can drive efficiently without collision and deadlock in the conflict zone. Experiment results show that the proposed cooperative operation approach can significantly alleviate the congestion problem in the AGV based parking lot and improve the AVP system’s efficiency.

Research on global optimization method for multiple AGV collision avoidance in hybrid path

AbstractDue to the increasing number of automated guided vehicles (AGVs) in the multi‐AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi‐AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi‐AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on. NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi‐AGV system.

An Improved Acceleration Method Based on Multi-Agent System for AGVs Conflict-Free Path Planning in Automated Terminals

Aiming at the problem that the increasing number of automated guided vehicles (AGVs) will lead to more frequent conflicts between AGVs. In this paper, a conflict-free path planning model for multi-AGV is established, aiming to minimize the blocking rate of AGVs between the quay crane and the yard crane, considering the travel speed, operation time, and conflict distance of AGVs. An architecture of AGV’s system based on Multi-Agent System (MAS) is designed, the improved interactive protocol based on blackboard model is used as the communication method of AGV, the improved acceleration control method is combined with the AGV priority determination method based on time cost as the negotiation strategy of AGV, the improved Dijkstra algorithm calculates the conflict-free path of each AGV. By comparing the acceleration control method based on MAS with the speed control method based on MAS and the task priority control method, the effectiveness of this method for solving multiple AGVs conflict-free path planning in automated terminals is verified.

A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises

In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.

Hierarchical planning for multiple AGVs in warehouse based on global vision

In automated warehouses, multiple automated guided vehicles (AGVs) are widely used to sort parcels for improving storage efficiency. However, as the number of AGVs increases, the scheduling complexity also increases dramatically. This paper proposes a hierarchical planning algorithm for efficiently coordinating a fleet of AGVs in warehouses based on global vision. We use the camera installed on the top of the warehouse to capture the global image that is used to track AGVs. Based on a network congestion diffusion model, we quantify the road congestion and add it to the evaluation index to form a time-varying dynamic evaluation function. Then the improved A* algorithm and time window algorithm are combined as the hierarchical planning algorithm to search the idle path and avoid collisions. Extensive simulations show that the hierarchical planning based on global vision not only effectively schedules multiple AGVs in the warehouse, but also has lower time complexity than other real-time path planning algorithms.