Path tracking of Automated Guided Vehicle

Gupta, Neerja; Singhal, Abhishek; K., J.; Kumar, Rohit

doi:10.1109/ic3.2014.6897183

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The FIREBIRD IV and ATMEGA microcontrollers (MCUs) were used to emulate the AGV while ZigBee was used to establish communication between a central controller and the AGV. In [146], authors applied a combination of laser-based tracking and pure pursuit algorithm for AGVs tracking in indoor environment. Movement of the AGV was determined using a mathematical model which was based on direct AGV kinematics.…”

Section: B Navigation Control and Guidancementioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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Section: B Navigation Control and Guidancementioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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“…Jiang and Zhang (2020) developed a wall‐climbing unmanned aerial system for real‐time crack detection (Jiang & Zhang, 2020). Research interest in the field of robotics has increased as a result of the challenge of route tracking and control of wheeled mobile robots (Gupta et al., 2014; Vans et al., 2014). Path‐following problems are often associated with mobile robots in industrial and manufacturing environments and in agricultural applications (Oliveira et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

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“…Besides, in their research, the laser and receiver SICK equipment (AUTO-NAV200) installed on the AGV rotating tower determines the automatic positron emission and direction. The experiment is carried out on a rectangular path and shows that the AGV can follow the path to reach the target position, with an acceptable position error of less than 5% [ 8 ]. The four-wheel mobile AGV structure with the fuzzy PID controller was studied by Liu et al In the simulation surrounding for tracing target, and as a result, they mentioned the AGV fast performance with the stability anti-interference in trajectory task [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

Moshayedi

Sina

et al. 2022

Computational Intelligence and Neuroscience

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Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group’s use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model’s ability for the researchers in robotic research.

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Path tracking of Automated Guided Vehicle

Cited by 6 publications

References 19 publications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

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