Autonomous Guided Vehicles Research Articles

Transitioning from AGVs to AIVs in integrated job shop scheduling with transportation tasks: a multi-agent simulator for comparative analysis

Optimizing job shop scheduling in modern factories demands flexibility and adaptability to handle unexpected events and Unmanned Ground Vehicles (UGVs) limitations. This paper addresses these challenges by introducing a novel multi-agent simulator for the Job Shop Scheduling Problem (JSSP) with UGVs handling transportation tasks. The simulator, designed with Netlogo, incorporates real-world constraints, such as collision avoidance, UGV fleet size, and battery limitations, often overlooked in prior studies. By comparing the two categories of UGVs, namely, Autonomous Guided Vehicles (AGVs) and Autonomous Intelligent Vehicles (AIVs), under different scheduling methods (static vs dynamic), we evaluate their performance in constrained manufacturing environments. Our findings highlight the superior performance of AIVs in terms of overall makespan and resilience to additional constraints. Among other results, we found that schedules with a fleet of 2 AIVs produced similar makespans than schedules with a fleet of 4 AGVs. Consequently, the simulator and the conducted study provide valuable insights for optimizing JSSP within constrained environments and making informed decisions regarding AIVs adoption.

Comparative Analysis of Filtering Techniques for AGV Indoor Localization with Ultra-Wideband Technology

This paper investigates the filtering techniques to enhance the accuracy of indoor localization for Autonomous Guided Vehicles (AGVs) using Ultra-Wideband (UWB) technology. A comprehensive comparative analysis of various filtering approaches, including the Kalman Filter (KF), Moving Average Filter (MA), Savitzky-Golay Filter (SG), Weighted Average Filter (WAF), and their combinations, are conducted. The primary focus of this paper is the integration of a Moving Average-Kalman Filter (MAKF) with an extended window size of 201. Experimental findings reveal significant performance differences among these filtering techniques. The most effective approach is the MAKF technique, achieving an accuracy of 85.13% and the lowest path deviation of 0.17 meters. Conversely, the MA exhibits the lowest accuracy at 68.83%. Notably, the WAF attains an accuracy of 72.46% but exhibits a significantly higher path deviation of 2.65 meters compared to 1.45 meters of the MA filtering technique. The proposed MAKF acknowledged for its ability to effectively reduce noise with real-time responsiveness, represents a significant advancement in AGV indoor localization techniques.

Rotating wireless power transfer: A new concept for wireless charging systems

Fast and safe charging are current concerns in relation to wireless charging of autonomous guided vehicles (AGVs). Wireless Power Transfer (WPT) is a capable solution and a replacement for conventional contact-based methods. However, resonant inductive and capacitive WPT topologies demand more power as the gap between transmitter and receiver pads increases. These structures, initially developed for electric vehicle applications, do not adequately optimize power transfer and power quality for the smaller surface areas of AGVs. In this paper, a novel topology for wireless charging concept is described and validated. This methodology has been named rotating WPT, with a primary side spinning neodymium magnet disc. The receiver pad is composed of a circular coil with ferrite U-cores. Calculations of the induced voltage over the rotating magnetic field resulted in 1.95 V at an operating frequency of 2.7 kHz. FEM simulation results show approximately 3 V, while laboratory experiments outputted 33 mV at 3.8 kHz. A sinusoidal waveform was observed from prototype, and power was successfully transferred to the receiver end, validating this proposed topology. This novel WPT system has no inverter, rectifier, nor compensation, decreasing reactive power, assembly costs and complexity while transferring power and enhancing power quality.© 2017 Elsevier Inc. All rights reserved.

An Automated Mechanised Robot for Handling Ricebags

A novel technology that overcomes the difficulties of manual labor has been created to increase productivity in large-scale food storage facilities. With an emphasis on rice bags in particular, this creative approach seeks to seamlessly replace human interaction in duties like picking, storing, moving, and monitoring food bags. The system uses an integrated approach that includes precision grippers, scissor lifts, Cartesian robots, autonomous guided vehicles (AGVs), and an advanced artificial intelligence-driven control system. Especially, the technology known as Simultaneous Localization and Mapping (SLAM) plays a crucial part in guaranteeing the smooth operation of the system. While Cartesian robots precisely perform complex jobs, autonomous mobility from the AGVs enables efficient and accurate movement within the storage space. Scissor lifts add to the system's flexibility in managing different storage arrangements. Rice sacks can be handled carefully and under control thanks to precision grippers. Artificial intelligence algorithms are employed by the overarching control system to facilitate the smooth coordination of various constituents. By combining these cutting-edge technologies, the system not only simplifies operations but also drastically lowers the need for manual labor, opening the door for a more effective and cutting edge method of managing food storage

Concave Ferrite Core for Wireless Power Transfer (WPT)

High-efficiency wireless power transfer (WPT) systems can present a perfect solution for fast-charging autonomous guided vehicles (AGV) to improve working hours in high-tech warehouses. Stationary charging stations reduce separation distance, improving coupling factor and power transfer efficiency. Analysis and design of the WPT system focused on maximum power at the load with a SS compensation circuit to reach high efficiency while applying the theory of power transformers design to maximize the power handleability with the physical dimensions. The proposed concept fits small AGVs. This paper proposes a unique ferrite structure for the transmitter ferromagnetic core. This novel shape introduces horizontal angular misalignment resistance due to the transmitter’s omnidirectional concave disc ferrite core combined with an E-core ferrite at the receiver side. The proposed WPT system can output 200 W at 100 kHz. A realistic 3D model has been designed into a symmetrical equivalent to reducing complexity and computational effort. The visualization of the magnetic flux distribution demonstrated that the proposed design has a better path to flow without concentrating flux in small regions, reducing heating losses.

Motion Simulation of Autonomous Guided Vehicles

Autonomous Guided Vehicles (AGVs) using omnidirectional wheels can change their moving direction flexibly without the use of steering wheel. Is it possible that a vehicle with standard pneumatic wheels (as in a normal vehicle) change its moving direction flexibly by the technique of regulating the driving torque supplied to each wheel? In this article, the simulated motion trajectories of the two types of vehicles were presented, that are the vehicle using Mecanum wheels and the vehicle using standard pneumatic wheels (normal wheels). For vehicles with normal wheels, the simulation was carried out in two cases of motion direction control: with and without using a steering wheel. Further, the main loop of the motion direction control algorithm by regulating the driving torque to the wheels was also presented for a prototype of a vehicle with normal wheel, which was used to simulate the motion direction control experimentally. The topic will focus on motion simulation and the comparison of simulation results to evaluate the application ability of the mentioned technique in AGVs as well as in passenger cars.

Federated Learning for improved prediction of failures in Autonomous Guided Vehicles

Autonomous Guided Vehicles (AGVs) are nowadays an indispensable component of production lines in smart manufacturing. Managing the fleet of AGVs covers not only the delegation of operational tasks but also the monitoring of AGVs activity and health condition by applying tailored Machine Learning-based methods to detect anomalies in various signals gathered by edge IoT devices mounted on board. Detecting anomalies requires appropriate prediction of selected signals based on multiple types of sensor readings. Momentary energy consumption is one of the signals that can indicate abnormal states in AGVs. In this paper, we show that the prediction of this signal can be improved with the Federated Learning (FL) approach that involves exchanging experience gained by particular AGVs. This paper significantly extends the conference paper (Shubyn et al., 2022) with the new multi-round approach to building global prediction models and recent experiments on real data streams produced by AGVs designed by the AIUT company. The results of our experiments prove that in the AGV operational environments with distributed knowledge Federated Learning performs better than traditional centralized approaches and that frequent synchronization of experience may lead to better prediction quality.

Smart and flexible manufacturing systems using Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT)

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

A Systems Engineering Approach for the Design of an Omnidirectional Autonomous Guided Vehicle (AGV) Testing Prototype

This paper addresses the mechanical and electrical design of an autonomous guided vehicle (AGV) test prototype based on a systems engineering approach. First, the different phases of the systems engineering approach are described. The conceptual design begins with the house of quality, which weighs the relevance of each user requirement and ends with a functional representation of the vehicle. Then, the mechanical and electrical design are presented considering different subsystems such as the chassis, cargo platform, suspension system, power, and control components. Finally, different tests were carried out on the prototype, validating its movement and load capacities. The systems engineering approach as a methodology for the construction of complex systems has proven to be an excellent tool for the development of autonomous guided vehicles.

Selection of optimal machine learning algorithm for autonomous guided vehicle’s control in a smart manufacturing environment

Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.

Strategy for the introduction of autonomous driving technologies: a case study in the logistics area of an automotive company

These days, due to the emergence of Industry 4.0, it is possible to notice a growing interest among companies in automating their processes. Autonomous driving technologies are generating increasing interest in carrying out material handling operations, being clear that these technologies allow companies to have greater flexibility, higher accuracy, lower costs, and higher productivity. What is not clear is how to implement these autonomous driving vehicles, which raises questions such as: What are the requirements to consider when using autonomous transport to move materials in the shopfloor? What are steps must be taken to introduce autonomous driving technologies in an industrial environment?This article, which describes a case study carried out in an automotive company, provides the answers to these questions, as well as presents a methodology to implement Autonomous Guided Vehicles (AGVs), being a relevant contribution to the understanding and implementation of AGVs in the industrial context. During the case study, the number of AGVs required was estimated (by analytical method and using SIMIO simulation software), and an economic feasibility study of the project was also performed.

Nonlinear Model Predictive Tracking Control With C/GMRES Method for Heavy-Duty AGVs

To ensure trajectory tracking accuracy and driving safety, this paper presents a real time nonlinear model predictive control (NMPC) method for autonomous guided vehicles (AGVs) to avoid dangerous load transfer effect in carrying heavy equipment. The AGV with a four-wheel-steering four-wheel-driving (4WS4WD) structure is controlled with a steer and velocity input for each wheel. The tracking problem is implemented by the standard NMPC framework and the referenced trajectory is planned by the Reeds-Shepp algorithm. A Large load creates a considerable load transfer effect which is dangerous for the wheels; hence, the controller limits the lateral acceleration to reduce load transfer. To realize online numerical computation for NMPC, the continuation/generalized minimal residual (C/GMRES) method is applied. A semismooth transformation based on the Chen-Harker-Kanzow-Smale (CHKS) function is introduced to handle inequality constraints since C/GMRES only solves standard equality forms. Trajectory tracking simulations are carried out in contrast to the quadratic programming method without lateral load transfer optimization, and practical experiments on an AGV carrying 13 tons of steel are also performed. The results indicate the accuracy, efficiency, safety and applicability of the proposed method on heavy-duty AGVs.

Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth

Digital technologies are transforming the industrial landscape and disrupting traditional business models. New business opportunities related to Industry 4.0 are emerging, so companies must adapt to the new environment. This work puts forward a multi-objective optimization algorithm to improve productivity and reduce the costs and energy consumption of autonomous industrial processes with the aim of achieving sustainable growth. The processes analyzed encompass an assembly line production with robotic cells and the subsequent material handling systems (MHS) using autonomous guided vehicles (AGVs) for indoor transport. An efficient algorithm has been implemented to integrate and minimize industrial robot arm working times, AGVs travel times and their trajectory, and the energy consumed in industrial processes while maximizing global business profits when manufacturing different products in an indoor industrial environment. Furthermore, this is carried out by considering the kinematics and dynamics of autonomous industrial processes and sustainable strategies to ensure compliance with government policies on environmental issues. These objectives are in line with the European Union (EU) guidelines on reducing greenhouse gas (GHG) emissions, renewable energy share, and improvements in energy efficiency for climate change mitigation and adaptation policies. Based on the difference in energy consumption between optimized and unoptimized industrial processes, the economic benefits can be quantified in terms of GHG emission quotas, volume of fuel consumed, and the indirect benefits with respect to improving corporate brand image. The methodology presented here has been successfully applied to several real case studies covering different manufacturing processes, robotic operations, and products. The results show that higher profits and sustainable growth are achieved when this methodology is used. It helps design Flexible Manufacturing Systems (FMS) and leads to shorter working times and higher energy efficiency and annual profits. In addition, Pareto frontiers show the trade-off between profits and product manufacturing times for different case studies.

Robotic industrial automation simulation-optimization for resolving conflict and deadlock

PurposeThe purpose of this study is to investigate the benefits of the turning point layout; a simulation model being applicable for strategic level is designed that compares systems with and without turning points. Specifically, the avoidance of deadlocks and prevention of conflicts is substantial.Design/methodology/approachOptimization process for different layouts and configuration of autonomous guided vehicles (AGVs) are worked out using statistical methods for design parameters. Regression analysis is used to find effective design parameters and analysis of variance is applied for adjusting critical factors. Also, the optimal design is then implemented in a manufacturing system for an industrial automation and the results are reported.FindingsThe outputs imply the effectiveness of the proposed approach for real industrial cases. This research will combine both simulation-based method and optimization technique to improve the quality of solutions.Originality/valueIn AGV systems, the begin-end combinations are usually connected by using a fixed layout, which is not the optimal path. The capability of these configurations is limited and often the conflict of multiple AGVs and deadlock are inevitable. By appearing more flexible layouts and advanced technology, the positioning and dispatching of AGVs increased. A new concept would be to determine each path dynamically. This would use the free paths for AGVs leading to overcome the conflicts and deadlocks.

Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

Lean manufacturing systems in the area of Industry 4.0: a lean automation plan of AGVs/IoT integration

Industry 4.0 represents a new industrial paradigm ignited by disruptive technologies that can transform manufacturing into a cyber-physical system that integrates products, people and processes. However, there is little guidance concerning how to implement and integrate Industry 4.0 technologies by existing lean manufacturing (LM) systems. We select autonomous guided vehicles (AGVs) and internet of things (IoT) to develop an action plan that helps managers integrate Industry 4.0 technologies into their manufacturing systems and achieve lean automation. We conducted a case study of a large manufacturing company that introduced AGVs and IoT to automate its lean operations. We used socio-technical systems (STSs) design logic to integrate the two distinct domains (lean and automation) into an action plan that successfully meets six lean automation objectives. The findings demonstrate that AGVs implementation should include three phases: design, integration and continuous improvement. The lean automation objectives are: cost, reusability, reliability, simplicity, compactness, fit, engage and culture. The lean automation plan successfully manages the interactions and interplay between social factors (people and culture), technical factors (infrastructure and technology) and operational factors (routines and processes). The lean automation plan has significant managerial implications helping companies integrate lean philosophy, which is people-centric, with Industry 4.0 technologies, which promote efficiency via automation.

A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario

A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.

Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation.

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm

Autonomous-guided vehicles (AGVs) are becoming increasingly prevalent and already helping in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors and microelectromechanical systems are bringing us closer to the realization of autonomous multi robot systems which perform complex tasks. In this work, we explored the problem of vehicle routing and motion planning for a fleet of AGVs in a flexible manufacturing system (FMS). Considering concurrently fuzzy demands associated with the workstations and fuzzy travel distances while moving between workstations, the problem is addressed in the context of uncertainty both in demands and travel times. The proposed motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. Furthermore, in order to take into account the fuzziness which may arise in the FMS, the proposed planner is integrated with fuzzy theory on fuzzy sets and fuzzy numbers. The efficiency of the solution procedure is demonstrated through numerical examples.