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With the application of in-house logistics automated guided vehicle (AGV) systems for transportation three different control problems arise: task assignment, empty vehicle balancing and routing. With an increasing fleet size and especially when considering requirements like flexibility and adaptivity these control problems often become complex to solve using a central controller. The main reasons are the increasing problem size and amount of information. Decentralized control of logistics systems has received a lot of attention during the last years and may help to remedy the shortcomings of central solutions. Decentralized solutions rely on a distributed implementation for decision making and make use of local information. This survey describes basics as well as general categorizations and reviews existing decentralized control strategies for the mentioned control problems of automated in-house logistics vehicle systems. If existing, decentralized solving approaches for the control strategy problems investigated by the scientific community are listed. Additionally, these control strategies are evaluated to which extend they fulfill the requirements of the decentralized paradigm.

Issue Title: Special Issue: Recent Advances on Control and Architectural Design of Robotic Systems Automated guided vehicles (AGVs) are used as a material handling device in flexible manufacturing systems. Traditionally, AGVs were mostly used at manufacturing systems, but currently other applications of AGVs are extensively developed in other areas, such as warehouses, container terminals and transportation systems. This paper discusses literature related to different methodologies to optimize AGV systems for the two significant problems of scheduling and routing at manufacturing, distribution, transshipment and transportation systems. We categorized the methodologies into mathematical methods (exact and heuristics), simulation studies, meta-heuristic techniques and artificial intelligent based approaches.

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.

The position or movement control of an automated guided vehicle (AGV) is crucial for its operation. However, choosing the AGV position sensor is not a trivial task. This paper investigates the sensors and sensing techniques in machine vision applied in AGV position control in the past 5 years of published academic research. Using a systematic literature review method, we seek to answer the main research question: which sensors and sensing techniques are used in indoor AGV positioning control problems according to the past 5 years of published research and their technological impact. In doing so, we address three sub-question: (i) is the sensor/sensing technique related to the AGV application area; (ii) is the sensor/sensing technique applied to the problem related to the control strategy and/or the AGV guide; (iii) is the sensor/sensing technique related to the required AGV accuracy/sensitivity level. The paper contributions are the application of a systematic method of literature review in AGV position sensors, the research area overview from the selected 31 articles of the past 5 years, and a research agenda proposal.

This paper studies the method for assigning workstations optimally in tandem automated guided vehicle system (TAGVS) with multiple automated guided vehicles (AGVs) in overload zone of transport. Workstations are assigned to zones by minimizing inter-zone and intra-zone flow and avoiding collision between AGVs. In TAGVS, only one AGV is assigned to each zone, so carrying load is not available or takes long time, in case of vehicle failure or overload of vehicle. This problem can be resolved by assigning multiple vehicles in such zone. This paper proposes a novel method for assigning workstations optimally in order to minimize inter-zone and intra-zone flow in accordance with building layout and transport flow in manufacturing process and avoid collisions between AGVs in overload zone with multiple AGVs. This method is compared with the architecture of traditional TAGVS. The proposed method is experimented at AGVS for a book printing process in Textbook Print Shop as the real world, and it is verified that this method is effective.

Automated Guided Vehicles (AGVs) are essential elements of manufacturing intralogistics and material handling. Improving the position accuracy along the AGV trajectory allows the vehicle to work on narrower aisles with lower error tolerance. Despite the increasing number of papers in AGVs and mobile robots’ position control research area, there is a lack of curatorial work presenting and analyzing the control strategies applied in the problem domain. Therefore, the main objective is to analyze the published researches of the past seven years on the position control of AGVs to recognize research patterns, gaps, and tendencies, outlining the research field. The paper proposes a systematic literature review to investigate the research field from the controller design perspective. Its protocol and procedures are presented in detail. Four main research topics were addressed: the control strategies used in the AGV position control problem, how the literature presents the AGV operating requirement of position accuracy, how the literature validate the proposed controller and present their results regarding the system’s position accuracy, and the technological tendencies the proposed solutions reveals. Besides, within the main topics, other points were investigated, such as the AGV application area, the considered mathematical model, the sensors and guidance system used, and the maximum payload of the vehicle and operation under different load conditions. The data synthesis shows the predominant control strategies applied to the problem and the interaction among distinct control theory areas, indicating a notable interaction of Intelligent Control techniques to the other strategies. The paper’s contributions are using a systematic literature review method over the AGV position control publications, presenting an overview of the research area, analyzing the research question topics from selected articles, and proposing a research agenda.

With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more in-depth discussion on its parameters’ tuning process. The authors usually do not describe it. This work aims to extend the analysis of the package’s parameters’ distinct influence in an automated guided vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper’s contribution is discussing the parameters’ variation impact on the AGV localization using the covariance matrix results. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default parameters which can improve the localization results, even modifying one parameter at a time.

Automated guided vehicle (AGV) scheduling has become a hot topic in recent years as manufacturing systems become flexible and intelligent. However, little research regards dynamic AGV scheduling considering energy consumption, particularly battery replacement. This paper proposes a novel method that employs deep reinforcement learning to address the dynamic scheduling of energy-efficient AGVs with battery replacement in production logistics systems. The bi-objective joint optimization problem of AGV scheduling and battery replacement management is modeled as a Markov Decision Process, which supports data-driven decision-making. Then, this paper constructs a deep reinforcement learning-based optimization architecture and develops a novel dueling deep double Q network algorithm to maximize the long-term rewards for optimizing material handling’s tardiness and energy consumption. Numerical experiments and a case study demonstrate that the proposed algorithm is more efficient and cleaner than state-of-the-art methods. The proposed method can significantly improve customer satisfaction and reduce production costs within flexible manufacturing processes, particularly in Industry 4.0.

PurposeThe Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.Design/methodology/approachDetails about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.FindingsThe resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.Originality/valueA solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.

Finding short and convenient routes for vehicles is an important issue on efficient operations of Automated Guided Vehicle (AGV) systems at container terminals. This paper proposes an anisotropic Q-learning method for AGVs to find the shortest-time routes in the guide-path network of cross-lane type according to real-time vehicle states, which includes current and destination positions, heading direction and the number of vehicles at anisotropic four-direction neighboring locations. The vehicle waiting time of AGV systems is discussed and its estimation is suggested to improve the policy to select actions in the Q-learning method. An improved anisotropic Q-learning routing algorithm is developed with the vehicle-waiting-time-estimation based selecting-action policy. The parameter settings and performance of the proposed methods are analyzed based on simulations. The numerical experiments show that the improved anisotropic Q-learning method can provide stable and dynamic solutions for AGV routing, and achieve 9.5% improvement in optimization efficiency compared to the Jeon Learning Method (Jeon et al. in Logist Res 3(1):19–27, 2011).