multi-AGV System Research Articles

Dynamic unbalanced task allocation of warehouse AGVs using integrated adaptive large neighborhood search and Kuhn–Munkres algorithm

Dynamic task allocation poses a complex and challenging decision problem for automated guided vehicles operating within warehouse environments. In this study, we investigate a new method for dynamically allocating unbalanced tasks to a multi-AGV system, with a focus on real-time task arrivals. The research treats this problem as a dynamic vehicle routing problem with pickups and deliveries and proposes the use of a rolling horizon strategy to periodically reallocate tasks by iteratively solving mixed integer programming. To enhance the computational efficiency, a novel metaheuristic is developed, which integrates adaptive large neighborhood search and the Kuhn–Munkres algorithm. Comprehensive numerical experiments are conducted to demonstrate the potential of the proposed approach, in comparison with state-of-the-art heuristics and metaheuristic algorithms, providing insight into the efficiency and effectiveness of the proposed dynamic unbalanced task allocation method for multi-AGV systems in warehouse environments.

Distributed predictive motion planning of automated guided vehicles: serial versus parallel schemes

The article presents two distributed motion planning methods for dynamically coordinating multiple automated guide vehicles (AGVs) in an industrial setting, aiming to enhance their flexibility, robustness and scalability. A predictive motion model is utilized to describe the transport process mathematically as a dynamical system. Subsequently, alternating direction method of multipliers (ADMM)-based decomposition techniques, both serial and parallel, are developed to coordinate the AGVs and mitigate the computational burden. The efficacy of the distributed methods is verified through testing in industrial scenarios, demonstrating that they can significantly improve multi-AGV system transport productivity with minimal computational effort. Notably, the parallel scheme exhibits superior performance in coordinating the AGVs compared to the serial scheme.

A scheduling optimization method for multiple automated guided vehicle systems

When constructing a multiple automated guided vehicle (multi-AGV) system, it is essential to calculate the potential values of the system’s feature parameters. This article presents two time-cost calculators and an improved simulation platform for scientifically estimating the cost of constructing a multi-AGV system. By inputting the parameters of the number of automated guided vehicles (AGVs), the velocity of the AGV, the number of tasks, scheduling interval and path length, the designed calculator can output the system’s time expense when all tasks are completed. The designed simulation platform can display a specific multi-AGV system’s time cost and how the entire progress works in a visual interface. An advanced scheduling method based on handover mode and A* algorithm is used to solve task scheduling problems in a multi-AGV system. The overall performance of the newly integrated scheduling system is compared with other scheduling systems to validate its superiority and shortcomings in different corresponding work scenarios. The final results present a robust solution to improve a multi-AGV system’s effectiveness.

Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

A Dynamic Task Assignment Optimization Method for Multi-AGV System Based on Genetic Algorithm

<p>Aiming at the influence of AGV without considering the working state on task assignment decision in multi-AGV system task assignment, a dynamic task assignment decision method with task completion prediction based on genetic algorithm. When assigning the arrived tasks at each stage, the decision method brings the working AGVs and the idle AGVs into the set of schedulable vehicles at the same time, which expands the scope of the optimal decision, makes the available AGV resources more fully mobilized in the dynamic scheduling process, and improves the efficiency of the whole scheduling system. First, this paper establishes a prediction model for task completion. On this basis, the task assignment decision model of multi-AGV system based on task completion prediction is established, and the coding, fitness function and genetic operation of the genetic algorithm suitable for this problem are designed. Finally, a univariate factor analysis is carried out on the task assignment time interval and the number of AGVs by using an example, which verifies the effectiveness of the task assignment strategy of the multi-AGV system based on task completion prediction. The results show that the genetic algorithm can better solve the task assignment problem with task completion prediction, and can schedule the available AGV resources to a greater extent, which effectively increase the number of tasks completed by the multi-AGV system in one production cycle.</p> <p> </p>

An ETCEN-Based Motion Coordination Strategy Avoiding Active and Passive Deadlocks for Multi-AGV System

In recent years, automated guided vehicles (AGV) are widely used to sort and transport parcels in logistics warehouses. The deployment of AGVs can improve storage efficiency and free human labour greatly. However, as the number of AGVs grows, the computational complexity and deadlock occurrence rate increase simultaneously, making it extremely difficult to coordinate AGVs’ movements in real time. In this paper, we first present a hierarchical motion coordination system based on event-triggered colored elementary net (ETCEN). The primary aim of the system is to coordinate AGVs in real time regardless of the system’s scale. Then, we describe deadlocks by the ETCEN model and classify them into two categories - active deadlocks and passive deadlocks. Active deadlocks are prevented dynamically by controlling the movements of AGVs, while passive deadlocks are resolved by an improved path planning strategy. The entire system relies on a two-layer architecture, thereby improving flexibility and scalability. The proposed algorithms are validated by simulations and applications. Experiment results demonstrated that our approach can coordinate multi-AGV systems, avoid collisions and prevent deadlocks effectively. Note to Practitioners—This paper was motivated by the problem of coordinating multi-AGV system in warehouses, especially the tricky problem of preventing deadlocks in large-scale applications. Existing approaches mainly focused on collision-avoiding strategies, while paying less attention to the deadlock-preventing problem. As a result, the feasibility of current deadlock prevention methods strongly depends on the topology of the environment, and the computation time will surge exponentially as the scale of AGVs grows. This paper suggests a general approach towards solving two types of deadlocks (active deadlocks and passive deadlocks) based on Petri net theory. In this paper, a multi-AGV coordination model is presented and described thoroughly. Then we use the model to prove our theories and introduce our collision-avoiding and deadlock-preventing strategies. A two-layer architecture is designed to support the expansion of AGV fleet, making our system highly scalable. Both experiments and applications suggest that this approach is feasible and effective. In future research, we will focus on further optimizing the coordination and routing algorithm to improve the system throughput.

A dynamic integrated scheduling method based on hierarchical planning for heterogeneous AGV fleets in warehouses.

In modern industrial warehouses, heterogeneous and flexible fleets of automated guided vehicles (AGVs) are widely used to improve transport efficiency. However, as their scale and limit of battery capacity increase, the complexity of dynamic scheduling also increases dramatically. The problem is to assign tasks and determine detailed paths to AGVs to keep the multi-AGV system running efficiently and sustainedly. In this context, a mixed-integer linear programming (MILP) model is formulated. A hierarchical planning method is used, which decomposes the integrated problem into two levels: the upper-level task-assignment problem and the lower-level path-planning problem. A hybrid discrete state transition algorithm (HDSTA) based on an elite solution set and the Tabu List method is proposed to solve the dynamic scheduling problem to minimize the sum of the costs of requests and the tardiness costs of conflicts for the overall system. The efficacy of our method is investigated by computational experiments using real-world data.

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.

A Novel Multi-AGV Coordination Strategy Based on the Combination of Nodes and Grids

A great challenge in modern automated logistics is to coordinate multi-AGV system safely and efficiently. Current strategies which detect collisions and deadlocks mainly by nodes are not feasible in some scenes. With the aim of avoiding collisions and preventing deadlocks regardless of the layout of warehouses and the size of AGVs, in this letter we explore a coordination strategy combining nodes and grids, where nodes are physical locations of the environment and grids are tiny and independent spaces resulted from a virtual division of the environment. The proposed strategy guarantees the safety of the system and prevents 75%-100% deadlocks compared to traditional strategies. A fast grid calculating method is also presented to ensure real-time control of the multi-AGV system. The proposed strategies are validated by simulations and applications. Experiment results demonstrated that our approach can avoid all potential collisions and prevent more than 75% deadlocks, increasing the overall efficiency greatly.

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.

Artificial neural network to predict traction performance of autonomous ground vehicle on a sloped soil bin and uncertainty analysis

• ANN models were developed to estimate the AGV’s traction behavior on slopes. • Uncertainty analysis quantified the reliability and robustness of ANN model output. • ANN is a promising, robust, and reliable method to predict off-road vehicle’s traction performance. • Predictive traction models would empower the multi-AGV’s operation on a high slope. A fleet of autonomous ground vehicles (AGV) is envisioned to expand farming to arable land suitable for production except for being too steep for conventional equipment. The success of proposed multi-AGV system largely depends on the traction performance of the individual AGVs on unevenly sloped terrain and optimization of the AGVs control variables. Therefore, the drawbar pull performance of a prototype AGV was evaluated in a soil bin at varying slopes, speeds, and drawbar pull (DP). The AGV’s traction performance was expressed in three metrics: tractive efficiency (TE), travel reduction ratio (TRR), and power number (PN). Optimizing the control variables is intricate and ill-defined, which requires an accurate model to predict the performance of the proposed multi-AGV system. Hence, this study aims to design an artificial neural network (ANN) to estimate the traction behavior of the AGV on a sloped testbed as a function of AGV’s speed, applied DP, and slope. A multi-layer perceptron feed-forward ANN architecture with a single hidden layer trained with a back-propagation algorithm was adopted. A series of ANN models with increasing complexity and different hidden layer activation functions were developed for each response variable, i.e., ANN-TE, ANN-TRR, and ANN-PN. A re-sampling-based method, K-fold cross-validation, was employed to estimate the model generalization error. The model success was evaluated via Mean Squared Error (MSE) and the Coefficient of Determination ( R 2 ) against a test set. The final predictive model was trained on the entire data set, and the observed R 2 was 0.933, 0.882 and 0.858, respectively, for ANN-TE, ANN-TRR, and ANN-PN. Subsequently, a Monte-Carlo Simulation based uncertainty analysis was carried out to demonstrate the model strength and the degree of uncertainty by constructing a 95% prediction interval. This study shows ANN as a promising, robust, and reliable method to predict traction performance in agricultural tillage-traction studies and developed models can empower the multi-AGV system on steep-uneven slope terrain.

Multi-AGV Tracking System Based on Global Vision and AprilTag in Smart Warehouse

Multi-AGV Tracking System Based on Global Vision and AprilTag in Smart Warehouse

Experimental Investigation on Traction, Mobility, and Energy Usage of a Tracked Autonomous Ground Vehicle on a Sloped Soil Bin

HighlightsLaying the groundwork for AGV mobility models for high slope terrain operations.AGV drawbar pull performance was evaluated on a level terrain, uphill, and downhill slopes up to 18° on a soil bin.AGV generates the optimum power efficiency with enough drawbar pull to perform a range of agricultural operations on uphill and downhill slopes up to 18°.Explored the suitability and established the boundary conditions of small size ground vehicles on the high slope farming.Generated sloped traction data would empower the multi-AGV system on sloped terrain.Abstract. Excessive steepness of grasslands, hills, or uneven terrain presents difficulties for farming with large conventional equipment. Therefore, a fleet of Autonomous Ground Vehicles (AGV) is proposed to perform primary agricultural operations on high sloped hills or terrain. However, it is imperative to understand how an individual AGV functions on sloping terrain under varying load and speed. Hence, this study aims to investigate the traction, mobility, and energy consumption characteristics of AGV on a sloped soil bin environment. A drawbar pull performance of the prototype AGV was evaluated on a level terrain and variable slope of 10° and 18°, both uphill and downhill, at varying drawbar pull (P) and AGV speed. The AGV’s performance metrics include power efficiency (PE), travel reduction (TR), and power number (PN) which relates to AGV’s traction, mobility, and energy usage, respectively. The AGV generated drawbar pull equivalent to its weight only on downhill run for reduced PE. On a level terrain (0°), the peak PE was 0.20 and was found to be 108.3% and 328.6% higher on 10° and 18° downhill run than uphill with 55.5% and 133% increase in drawbar pull, respectively. Both applied drawbar pull and uphill operation caused the AGV’s TR. The TR, corresponding to a peak PE, increased from 10% to 30%, respectively, on 0° and both 10° and 18° uphill. The optimum values of power number ranged from 2 to 4. The AGV delivers the optimum PE and generates enough drawbar pull with an optimum TR to perform a range of agricultural operations on a slope up to 18°. This study explored the suitability and established the boundary conditions of small size ground vehicles for high-sloped farming. Besides this, the study also aims to generate an AGV’s slope traction database to optimize its control variables, design optimization, and develop a mobility model for sloped terrain. Keywords: Drawbar pull, Ground vehicle, Multi-AGV fleet, Power efficiency, Slope, Travel reduction.

Dynamic Spare Point Application Based Coordination Strategy for Multi-AGV Systems in a WIP Warehouse Environment

Multi Automated Guided Vehicle (multi-AGV) systems are widely used in Work-in-Process (WIP) warehouses to improve the efficiency of material transportation. However, collisions and deadlocks between AGVs are inevitable. Many algorithms have been proposed to solve these problems, but their performance is inefficient in the WIP warehouse due to the lack of consideration of its features. In this paper, to fill the gap between current research and real-world application requirement, we construct a collision and deadlock solving model for a multi-AGV system in a WIP warehouse (CDSMWW). Then we propose a coordination strategy based on dynamic spare point application (DSPA), aiming to improve the efficiency of solving AGV collisions and deadlocks in a WIP warehouse. In this method, the AGV records its predecessor, successor, and reserved points in its local controller and can only enter the set of sequential shared points if its application for spare points is successful. Otherwise, it has to stop to avoid causing an unresolvable deadlock. If a deadlock occurs while this AGV is in the sequential shared points, the AGV moves to the spare point to resolve it. Whenever an AGV leaves the set of consecutive shared points, it releases all its spare points. We apply the algorithm to both the central and local controllers to improve the calculation efficiency. Extensive simulation results demonstrate the feasibility of the DSPA scheme under realistic WIP warehouse environment and its higher efficiency compared to previous state-of-the-art methods.

Research on pull-type multi-AGV system dynamic path optimization based on time window

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

The Scheduling Technology Development of Multi-AGV System in AI Era

As an important intelligent transportation equipment of logistics, Multi-AGV scheduling is a key problem that restricts the operation efficiency and large-scale expansion of AGV system. This paper analyzes and elaborates three key technologies of AGV scheduling, namely Path Planning, Task Scheduling and Traffic Control Management. The method of Time Window modeling based on physical entity is superior to that based on particle motion. Finally, the trend of AGV scheduling intelligentization is illustrated by combining with the current advanced technology.

A Probabilistic Time-Constrained Based Heuristic Path Planning Algorithm in Warehouse Multi-AGV Systems

This paper mainly focuses on the path planning algorithm of multi-AGV system in the warehouse environment. We first analyze and model the path network of multiple AGVs based on dynamic stochastic network theory. Then, a probabilistic time constraint is added in the process of the well-known A⁎ heuristic algorithm, and the solution of the time cost is proposed based on probability theory. Furthermore, a multi-AGV conflict avoidance strategy suitable for heuristic planning algorithms is achieved in combination with queuing mechanism. Finally, numerical simulation experiments of the warehouse multi-AGV system are realized and demonstrate the effectiveness of the proposed algorithm.

Waste Smoke Recovery Based on Time Window Path Optimization of Multi-AGV System

In this paper, a floor fork laser guided transport vehicle is used to recover waste smoke. The advantages are obvious, small and flexible, safe and reliable, and it is widely used in working places with narrow space and high level difference. In the multi-AGV system, each AGV coordinated with each other to complete the waste smoke recovery. When multiple AGVs are running at the same time, it is necessary to consider path planning issues to avoid conflicting paths. On the one hand, the operation efficiency is affected, and on the other hand, the AGV system is paralyzed. Multiple AVGs run in the system at the same time and perform multiple handling tasks at the same time. Coordination is required to avoid or preempt the area first, avoid path conflicts, and collaborate to achieve efficient distribution.

Optimization of Waste Smoke Recovery Scheduling Strategy Based on Multi AGV

AGV is an important material handling equipment for flexible, lean and intelligent production of smart factory. Because of the high cost of AGV car and the congestion of vehicles in the system, this chapter studies the waste smoke recovery path planning and material handling task scheduling strategy of multi AGV system to improve the waste smoke recovery efficiency of AGV.

A prioritized planning algorithm of trajectory coordination based on time windows for multiple AGVs with delay disturbance

PurposeIn the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.Design/methodology/approachThis paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.FindingsThe simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.Originality/valueBy introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.