Path Planning Algorithm Analysis of Multiple AGV

This paper addresses the problem of mapping and localization for optically guided Automated Guided Vehicles (AGV) that are widely needed in the warehousing industry. It describes an approach to build a floor plan of the optical path traversed by the AGV, while keeping the process cost-effective by using the AGVs internal sensors and the optical sensor used to keep track of the path. The idea is to create a distributed system where each AGV knows its location in the warehouse and is capable of calculating an optimal path when given a destination without having to map the environment prior. This is done through fusion of maps from multiple AGV. Each AGV creates its own map and communicates it to the mapping module which collects maps from all the AGV in the system and creates a fused map. Such a map needs to be robust against errors and noise from multiple AGV.

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.

This paper addresses the problem of cooperative transportation using multiple omnidirectional Automated Guided Vehicles (AGVs). To enhance flexibility and application potentials, mecanum-wheeled platform is considered for the proposed multi-AGV system while the cooperative transportation is executed without physical link/gripper to fix the object on the AGVs. Therefore, the position and number of AGV is adjustable depending on the size and weight of the transported object. Analysis of force distribution to each AGV during cooperative transportation is presented. Furthermore, the gradient projection method is exploited to regulate internal force according to the operational capability of each AGV. Moreover, an adaptive sliding mode controller is designed for AGV to cope with dynamic uncertainty during cooperative transportation. Stability of the proposed controller is proven by using Lyapunov Theorem. Finally, numerical simulation is presented to demonstrate the performance of the proposed control system.

We herein try to schedule multiple AGVs (Automated Guided Vehicles) in real time with two-phase strategy in the flexible manufacturing workshops. This study considers the impact of running time, vehicle stopping and turning of AGVs, and deals with static workshop scheduling, real-time workshop scheduling with time-window based micro genetic algorithm. And we present a two-stage scheduling strategy for offline shortest path library generation and online optimal scheduling scheme generation. The preliminary experimental results showed the efficiency and stability of the proposed strategy and algorithm for Multiple AGVs system.

In today’s intelligent warehouses, automated guided vehicles (AGVs) are widely used, and their scheduling efficiency is crucial to the overall performance of warehouse business. However, AGV scheduling is a complex problem, especially when there are a large number of tasks to be undertaken by multiple AGVs in a large warehouse. In this paper, we present a problem of scheduling multiple AGVs for order picking in intelligent warehouse, the aim of which is to minimize the latest completion time of all orders. After testing a variety of algorithms, we propose a hybrid water wave optimization (WWO) and tabu search (TS) algorithm for efficiently solving the problem. We test the algorithm on a set of problem instances with different sizes, and the results show that the proposed algorithm exhibits significant performance advantages over a number of popular intelligent optimization algorithms.KeywordsAutomated Guided Vehicle (AGV)Intelligent schedulingWater Wave Optimization (WWO)Tabu Search (TS)Routing

An automated manufacturing system (AMS) contains a number of versatile machines (or workstations), buffers, an automated material handling system (MHS), and is computer-controlled. An effective and flexible alternative for implementing MHS is to use automated guided vehicle (AGV) system. The deadlock issue in AMS is very important in its operation and has extensively been studied. The deadlock problems were separately treated for parts in production and transportation and many techniques were developed for each problem. However, such treatment does not take the advantage of the flexibility offered by multiple AGVs. In general, it is intractable to obtain maximally permissive control policy for either problem. Instead, this paper investigates these two problems in an integrated way. First we model an AGV system and part processing processes by resource-oriented Petri nets, respectively. Then the two models are integrated by using macro transitions. Based on the combined model, a novel control policy for deadlock avoidance is proposed. It is shown to be maximally permissive with computational complexity of O (n2) where n is the number of machines in AMS if the complexity for controlling the part transportation by AGVs is not considered. Thus, the complexity of deadlock avoidance for the whole system is bounded by the complexity in controlling the AGV system. An illustrative example shows its application and power.

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

With the continuous development of intelligent robot, the application of Automated Guided Vehicles (AGV) increasingly becomes popular in many industrial fields. However, there are a series of problems in Multi-AGV System, such as resource allocation, conflict and deadlock. It is hard to find a perfect path for each AGV without conflict and collision in Multi-AGV System. In this paper, a Multi-AGV task assignment system is established by using the time window method and we find the optimal path by the A* algorithm. In addition, the system is realized by programming and a simulation experiment. The simulation results show that the system can effectively solve the conflict problem of AGVs, and is stable and high real-time.

An automated warehouse system contains a number of materials, workstations, and multiple Automated Guided Vehicles (AGVs). The automated warehouse is server-controlled. This paper proposes a collision-free routing method for AGVs based on collision classification. This method can deal with collisions arising in the automated warehouse. It first divides the warehouse environment into five areas, and then performs route planning. In this paper, the environment map for AGVs is described by using the grid method. The initial route of each task is predetermined by improved Dijkstra’s algorithm. The server detects the potential collisions by comparing each workstation’s ID and corresponding time window in every route. This paper presents four collision classifications and three solutions. Based upon the analyses and experiments, we select the corresponding solution for each type of collision. Presented case studies demonstrate the efficiency of the proposed collision-free route planning approach.

Intelligent Logistics is one of most important strategic directions for development of modern logistics industry. Automated guided vehicle (AGV) is key equipment benchmark for development of Intelligent Logistics. This paper analyzes development history of AGV and their current application status, both at home and abroad. It also points out AGV’s new direction for development and advancement of China’s Intelligent Logistics where we should focus on research and promotion of AGV since they are artificial intelligence-oriented and based on needs of Intelligent Logistics. In future, combination of robot and autopilot technology will greatly enhance application and service levels of AGV to Intelligent Logistics, reduce logistics costs of trunk transportation and the last mile, improve efficiency of logistics transportation and distribution, and promote user’s experience.

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.

Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity.

Path planning is one of the most important fields of research in the area of robotics. In this paper, a path planning method for a certain type of wheeled mobile robots called automated guided vehicles (AGVs) is proposed. The proposed model applies fuzzy control techniques to navigate the AGV in an unknown environment to reach a certain destination. In addition, a new method for escaping local minimums, which may be very critical issue faced by an AGV in an unknown environment, is introduced. Simulation results show satisfactory performance of the proposed method

In order to solve the path planning problem of Automated Guided Vehicle (AGV) transporting goods and packages in warehouse logistics, this paper studies the Algorithm of the path planning problem, an improved adaptive genetic algorithm is presented to solve the path optimization problem. In order to avoid falling into local optimum, an improved self-adaptive crossover method is proposed. In order to avoid the conflict of AGV in the process of path planning, the concept of Congestion Coefficient is introduced into the design of fitness function, reduce the AGV in the path optimization process of conflict. The mathematical model of AGV to accomplish multi-task is established, and the comparative experiment is set up. The experimental results show that the improved adaptive genetic algorithm can improve the optimization performance by 4.2% in solving the optimal path problem, and improve the convergence speed by 4.2%, the improved algorithm has obvious advantages.

To address the issues of insufficient safety distance and unsmooth paths in AGV path planning for narrow road scenarios, this paper proposes a method that integrates Voronoi-skeleton-based custom layers with traditional cost maps. First, key nodes of the Voronoi skeleton are extracted to generate a custom layer, which is then combined with static, obstacle, and expansion layers to form a new four-layer network cost map. This approach accurately distinguishes obstacle influences and enhances algorithm robustness. The A* algorithm based on this new map guides the automated guided vehicle (AGV) to travel safely along the road center. Second, an improved A* algorithm is employed for global planning to ensure safe navigation. Finally, B-spline smoothing is applied to the global path to enhance the AGV's efficiency and stability in complex environments. The experimental results show that in narrow road scenarios, the proposed algorithm improves AGV path planning safety by 82%, reduces the number of spatial turning points by 55.85%, and shortens planning time by 48.98%. Overall, this algorithm significantly enhances the robustness and real-time performance of path planning in narrow roads, ensuring the AGV moves safely in an optimal manner.