Autonomous Guided Vehicle System Research Articles

Implementation and evaluation of an assistance software to support decision-making, design and simulation setup for automated guided vehicle systems

Autonomous guided vehicle systems (AGVS) offer huge potential for optimizing transportation tasks in manufacturing and warehouse logistics. Especially for small and medium enterprises, this potential is left untapped, as the selection of a proper solution requires substantial expertise. Support from external consultancies is often not requested. To address this issue, concepts for expert systems and multi-criteria decision algorithms have been discussed in academia for years but have not found any practical application yet. Therefore, in this paper we present an easy-to-use, holistic planning tool implemented with a user-centered development process, followed by the evaluation of the prototype. The results show its feasibility to realize a comprehensive planning process in such way, that users with little or no specific prior knowledge can perform initial steps for planning AGVS on their own. The user tests indicate good usability and acceptance for the prototype and show improvement potential for further development iterations.

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.

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.

Automatic Simultaneous Extrinsic-Odometric Calibration for Camera-Odometry System

This paper focuses on the simultaneous extrinsic-odometric calibration of a mobile robot system equipped with odometric devices and a monocular camera. Most current approaches are based on either optimization or Gaussian filter, which depends on a manually provided initial guess. In this paper, we propose a two-step fully automatic calibration algorithm, which does not require any prior knowledge of the un-calibrated parameters. In the first step, both the odometric parameters and the extrinsic ones are estimated through a non-iterative auto initialization process. In the second step, a joint optimization problem is solved iteratively to obtain a refined calibration result. By exploiting the planar motion constraints of the landmark measurements, our auto initialization method outperforms a comparison approach in robustness against the image noise. Experiments are conducted with data sets collected from both simulation and an autonomous guided vehicle system, which validates the improvement.

A decentralized control strategy for the coordination of AGV systems

This paper deals with the complex problem of controlling and coordinating Autonomous Guided Vehicles (AGV) by a decentralized approach. Each AGV selects its route by a consensus algorithm based on some Integer Linear Programming problem solutions. Moreover, the AGVs move inside a zone-controlled guidepath network and coordinate their movements by a decentralized protocol based on a zone-controlled approach, which guarantees the avoidance of deadlocks and collisions. The proposed decentralized strategy is applied to a guidepath network by means of a simulation software.

AGV Trajectory Control Based on Laser Sensor Navigation

Autonomous Guided Vehicle Systems (AGVs) are used to transport goods and products in manufacturing fields where navigation can be done in a structured environment. In order to track the given trajectory, a tracking control based on Lyapunov stability theory is introduced. The use of the nonlinear Lyapunov technique provides robustness for load disturbance and sensor noise. To apply Lyapunov's theorem, the kinematic model of AGV is given. To recognize its position in indoor environment, in this paper, a laser sensor device NAV200 is used to detect the AGV position in real-time. For simulation and experiment, software and hardware are described. The AGV consists of 4 wheels with two passive wheels and two driving wheels. A controller is developed based on industrial computer. The effectiveness of the proposed controller is proved by simulation and experimental results. [AGV Trajectory Control, Laser Sensor Navigation]

위성항법 기반 AGV(Autonomous Guided Vehicle)의 조향 성능 시험

본 논문에서는 위성항법 기반의 위치 정보만을 이용하여 저속으로 운행하는 이동체의 제어 성능을 확인하기 위해서 골프장에 무인으로 운행하는 AGV(Autonomous Guided Vehicle)를 위성항법 기반의 AGV로 구성하여 조향 시험을 수행하였다. 이를 위해 기존 AGV 시스템의 구성에 대한 분석을 수행한 후 위성항법 기반의 위치 정보를 이용하여 조향 제어가 가능하도록 제어기 및 조향 제어 알고리즘을 개발하였다. AGV의 조향 성능을 알기 위해서 직선과 원형으로 이루어진 기준궤적을 생성하여 시험을 수행하였으며 시험 결과 기준궤적에서 <TEX>${\pm}0.2m$</TEX> 범위 안으로 조향 제어가 가능함을 확인하였다. In this paper, a GNSS-based AGV system was designed, and steering tested on a golf cart using electric wires in order to confirm the control efficiency of the low speed vehicle which used only position information of GNSS. After analyzed the existing AGVs system, we developed controller and steering algorithm using GNSS based position information. To analyze the performance of the developed controller and steering algorithm, straight-type and circle-type trajectory test are executed. The results show that steering performance of GNSS-based AGV system is <TEX>${\pm}\;0.2m$</TEX> for a reference trajectory.

Driving control of an AGV for an automated container terminal using an immunized PID controller based on cell-mediated immunity

In this article, an immunized PID (I-PID) controller using a cell-mediated immune algorithm (CMIA) is proposed to control an autonomous guided vehicle (AGV) more effectively. The proposed controller is based on the specific immune response of the biological immune system that is cell-mediated immunity. Moreover, we designed and manufactured an AGV system for an automated container terminal (ACT), and some experiments are shown to verify the performance of the proposed controller. The tracking error of the AGV driving was investigated for this purpose. As a result, the capabilities of realization and reliability were proved by comparing the response characteristics of the proposed I-PID controllers with those of conventional PID and neural network PID (NNPID) controllers.

An adaptive control of an autonomous guided vehicle system using cell-mediated immune algorithm controller and vision sensor

In this paper, we proposed an adaptive control method which is named CMIA (cell-mediated immune algorithm) controller with PID scheme. It is based on specific immune response of the biological immune system which is the cell-mediated immune response. It is also applied for the autonomous guided vehicle (AGV) system which is manufactured in this paper. The AGV is used for the port automation to carry container without human and to overcome uncertainty and nonlinearity because of running in the outdoor. To verify the performance of the proposed CMIA controller, some experiments for the AGV system are performed. Finally, the experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

다중무인운반차 시스템의 새로운 동적경로계획 알고리즘 : 비정지우선 우회 알고리즘

We present a new dynamic routing scheme for multiple autonomous guided vehicles (AGVs) systems. There have been so many results concerned with scheduling and routing of multiple AGV systems; however, most of them are only applicable to systems with a small number of AGVs under a low degree of concurrency. With an increased number of AGVs in recent applications, these AGV systems are faced with another problem that has never been occurred in a system with a small number AGVs. This is the stop propagation problem. That is, if a leading AGV stops then all the following AGVs must stop to avoid any collision. In order to resolve this problem, we propose a nonstop preferential detour (NPD) algorithm which is a new dynamic routing scheme employing an election algorithm. For real time computation, we introduce two stage control scheme and propose a new path searching scheme, k-via shortest path scheme for an efficient dynamic routing algorithm. Finally, the proposed new dynamic routing scheme is illustrated by an example.

TRAFFIC ANALYSIS OF A NETWORK-BASED CONTROL SYSTEM USING THE CAN

This paper deals with an architecture of network-based control system using the CAN(Controller Area Network) protocol and its traffic analysis. It is difficult to determine an optimal network-based control architecture for a specific AGV(Autonomous Guided Vehicle) system with a manipulator arm. The fixed number of periodic messages to be occurred is pre-defined in the system. To determine whether the proposed system architecture is effective or not, we perform traffic analysis for the real-time communication of all messages. Through simulations, the range of transmission speed is found satisfying the required conditions and the permissible number of additional sensors is investigated for improving the system performance, when the sampling periods of analog sensors are determined under fixed condition that the transmission speed is over 500Kbps.

Application of a real time expert system platform for flexible autonomous transport in industrial production

In a manufacturing environment, material transport plays a key role for the production process efficiency. In Schoeller-Bregenz (SB), a textile manufacturing SME, induction wire guided vehicles performed typical material transports, without the flexibility to adapt transport routes to changes in the production process. To overcome these deficits in the RETRARO project 8 The RETRARO project (Ref. 20.778) is partially funded by the ESPRIT IV Programme of the European Commission. RETRARO Consortium is formed by GMV, UPV, ARS, FH Ravensburg-Weingarten and Schoeller-Bregenz. 8 an Autonomous Guided Vehicle (AGV) was designed and tested in the production plant. To adapt to changing production configurations, RETRARO had to provide solutions for the following functionality regarding economical constraints: algorithms for efficient path planning and health monitoring; economic sensor systems to provide navigation and obstacle avoidance functions; Integration of free navigation capabilities with existing wire guidance systems. This paper presents a survey on the AGV system design and details on the implementation of the control system based on: the real time expert system REAKT for path planning and health monitoring; a low cost ultra sonic range detection system using sensor data fusion for navigation and obstacle avoidance.