Automated Guided Vehicle Navigation Research Articles

Study on automated guided vehicle navigation method with external computer vision

Automated guided vehicle (AGV) navigation is extensively used in industrial manufacturing. Existing AGV navigation methods have high accuracy but usually require expensive positioning sensors. This paper proposes a novel method for AGV navigation based on external computer vision (NECV). No matter how many AGVs are in the workshop, the proposed NECV method uses only an external camera mounted on the top of the roof to detect and track AGVs, and all the AGVs don’t need to be equipped with any positioning sensors. Because there is no need to equip positioning sensors on AGVs, and also don’t need to arrange positioning signs, NECV significantly reduces the positioning cost of navigation. YOLOv8 was selected as the detector for NECV, and the training was completed using a prepared dataset. We improved the structure of the StrongSORT algorithm and used it as the tracker. The improved StrongSORT algorithm is the core of NECV. The imaging coordinates of the AGVs are detected by the detector, transformed into global coordinates through inverse perspective mapping, and passed to the master console. Experimental results indicated that the NECV detection deviation q of the AGV and the experimental accuracy metrics of the NECV after compensating q were considerably improved, close to those of the popular Quick Response (QR) code navigation method. Statistically, NECV can reduce the cost of AGV positioning detection by 90%.

AGV robot for laser-SLAM based method testing in automated container terminal

PurposeMagnet spot is the primary method to develop the automated guided vehicle (AGV) guidance system for many automated container terminals (ACT). Aiming to improve the high flexibility of AGV operation in ACT, this paper aims to address the problem of technical stability leading to ACT production paralysis and propose a mini-terminal AGV robot for testing laser simultaneous location and mapping (SLAM)-based methods in ACT operation scenarios.Design/methodology/approachThis study developed a physical simulation robot for terminal AGV operations, providing a platform to test technical solutions for applying laser navigation-related technologies in ACTs. Then, the terminal-AGV navigation system framework is designed to apply the laser-SLAM-based method in the physical simulation robot. Finally, the experiment is conducted in the terminal operation scenario to verify the feasibility of the proposed framework for lased-SLAM-based method testing and analyze the performance of the different mini-terminal AGV robots.FindingsA series of experiments are conducted to analyze the performance of the proposed mini-terminal AGV robot for laser-SLAM-based method testing. The experimental results show the validity and effectiveness of the AGV robot and AGV navigation system framework with better local map matching, loopback and absolute positional error.Originality/valueThe proposed mini-terminal AGV robot and AGV navigation system framework can provide a platform for innovative laser-SLAM-based method testing in ACTs applications. Therefore, this study can effectively meet the high requirements of ACT for maturity and stability of the laser navigation technical.

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment

Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.

Pattern Recognition untuk Deteksi Posisi pada AGV Berbasis Raspberry Pi

Perkembangan teknologi di bidang otomatisasi dan robotika berkembang sangat pesat, karena memiliki tingkat efesiensi tinggi dari segi tenaga dan waktu. Pada sistem pergudangan, salah satu robot yang digunakan adalah Automated Guided Vehicle (AGV). AGV adalah alat transportasi berupa robot yang dikendalikan secara otomatis, yang berfungsi sebagai pengangkut barang, dengan menggunakan sistem navigasi agar bergerak ke arah yang telah ditentukan. Salah satu sistem navigasi AGV yang telah ada ialah dengan mengikuti pola garis pada lantai. Sistem tersebut kurang efisien karena lambat laun pola garis tersebut akan pudar dan tidak dapat terdeteksi kembali akibat gaya gesek dari roda AGV itu sendiri. Oleh karena itu, sangat diperlukan pengembangan sistem navigasi AGV untuk meminimalkan hambatan tersebut. Sistem pattern recognition ini menggunakan pola yang diletakkan pada langit-langit bangunan dan kamera sebagai sensor yang menghadap ke atas sehingga AGV mampu dengan leluasa mendeteksi pola. Kemudian, pola yang sudah terdeteksi diolah melalui perangkat komputer berupa Raspberry Pi 4 Model B yang telah diprogram. Hasil pengujian menunjukkan bahwa sistem ini mampu mendeteksi posisi dan berhasil menampilkan titik koordinat (x,y) dari AGV serta akan tetap berjalan sampai kapan pun hingga program diubah sesuai yang diperintahkan.

LIDAR EKF-SLAM Technology and Its Application in Automated Guided Vehicle Navigation

Automated Guided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF-SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation.

Design and Implementation System Automatic Guided Vehicle (AGV) Using RFID for Position Information

Manufactured goods distribution system is a very important part in the production chain. Delivery of goods from one point to another point affects the effectiveness of the production process. At the moment, most companies whose business are in manufacturing require automation, including distribution of goods. One application of automation in the distribution of goods is AGV (Automated Guided Vehicle). Companies need AGV that can move in complex pathways. The movement includes a selection of AGV path traversed and AGV terminal position. This research discusses the design and implementation of an AGV navigation and position information settings. AGV navigation uses fuzzy logic in its algorithm, while position setting of the AGV uses Radio Frequency Identification (RFID) to recognize the position of the robot at each terminal. From the testing result, the system obtained a success rate of 96% in movement of the robot from one terminal to another terminal using the RFID. RFID can be read at both speed of the robot i.e. 6.9 and 7.13 m/min

Design of Double Lifting AGV Control System

In this paper, we designed a double lifting AGV (Automated Guided Vehicle) control system, and applied in automobile powertrain assembly line and axle assembly. Front and rear drive dynamics wear adopted in wheels drive mode, steering gears wear used in body rotating. Tapes were used in AGV navigation. Wi-fi was accessed to wireless for data transmission between AGVs and scheduling center. AGV and auto assembly line used PSD synchronous track detection device.

Research on RFID and Vision-Based AGV Navigation

In order to implement vision-based navigation of AGV (Automated guided vehicle) with complex path, a method incorporating RFID (radio frequency identification) technology with image processing technology is proposed, in which RFID technology is used to recognize nodes and image processing technology is used to detect path. Straight line path is detected by Hough transform in case that AGV runs straightly, while curve path is detected by line scanning in case that AGV turns right and left. Experiment results show that, with the method presented in this paper, the navigation of path tracking for AGV is implemented well.

２台のカメラを用いたＡＧＶナビゲーションのための人工ランドマークの配置計画法と観測計画法

This paper proposes AGV (Automated Guided Vehicle) navigation methodology by using two cameras and artificial landmarks. This method doesn't need high cost sensors and doesn't have to model motion errors of AGV. It is proposed that (a) a landmark arrangement planning algorithm in a given working environment of the AGV, and (b) a landmark observation planning algorithm along a given path of the AGV. Navigation experiments with a real AGV show the effectiveness of the proposed methodology.

Intelligent collision avoidance by fuzzy logic control

A fuzzy approach to collision avoidance for automated guided vehicle (AGV) navigation has been proposed in our earlier work. By fuzzy inference, an AGV was guided from the starting point toward the target without colliding with any static obstacle as well as moving obstacle. In the present study, emphasis is on some difficult issues concerning AGV navigation, including sensor modeling and trap recovering. In sensor modeling, we wish to find the minimum number of sensors and their optimal arrangement on an AGV, so that the views of all angles can be seen by the AGV. In trap recovering, fuzzy logic and crisp reasoning are combined to guide an AGV to get out of a trap. Moreover, the AGV's ability to avoid collision with unknown moving obstacles is improved in the study. Simulation results are presented to show the feasibility of the proposed approach.

Collision avoidance by fuzzy logic control for automated guided vehicle navigation

AbstractA fuzzy approach to collision avoidance for automated guided vehicle (AGV) navigation is proposed. Static obstacles with no a priori position information as well as moving obstacles with unknown trajectories are considered in this study. Intuitive and subjective human ideas of collision avoidance are modeled into fuzzy rules. Fuzzy logic is applied in the inference procedure for AGV navigation, such that the AGV is guided from the starting point toward the target without colliding with obstacles. Furthermore, the proposed method can also be used for the navigation of multiple AGVs, where each AGV must avoid other AGVs as well as obstacles in the environment. Simulation results are presented to show the feasibility of the proposed fuzzy approach. © 1994 John Wiley & Sons, Inc.

Automated guided vehicle with absolute encoded guide-path

An automated guided vehicle (AGV) having the ability to recover its absolute position anywhere on the guide-path is described. It uses an original guide-path encoding technique, based on the properties of pseudorandom binary sequences, resulting in a minimum code complexity of 1 bit per quantization step. An experimental AGV system was built to test the proposed absolute position measurement method and its application for AGV navigation when unexpected obstacles are encountered on the guide-path. The results recommend this technique for implementation as a stand-alone function to cost-effectively upgrade existent optically guided industrial AGVs. >