Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

Control System Problem

Control System Problem

In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

Control Systems: Applications

Control Systems: Applications

Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

How Accurate Can 2D LiDAR Be? A Comparison of the Characteristics of Calibrated 2D LiDAR Systems.

Sensors (Basel, Switzerland)·2025

Same author

The Application of GPS-Based Friend/Foe Localization and Identification to Enhance Security in Restricted Areas.

Sensors (Basel, Switzerland)·2024

Same author

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects.

Sensors (Basel, Switzerland)·2023

Same author

Investigation of the Impact of Damaged Smartphone Sensors' Readings on the Quality of Behavioral Biometric Models.

Sensors (Basel, Switzerland)·2022

Same author

Selection of Surge Protection Module Components for Communication Lines Using a Genetic Algorithm.

Sensors (Basel, Switzerland)·2022

Same author

A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

Sensors (Basel, Switzerland)·2021

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Controlling AGV While Docking Based on the Fuzzy Rule Inference System.

Damian Grzechca¹, Łukasz Gola¹, Michał Grzebinoga¹

¹Department of Electronics, Electrical Engineering and Microelectronics, Silesian University of Technology, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This study presents a software-only solution to improve Autonomous Guided Vehicle (AGV) docking accuracy. A novel fuzzy logic controller enhances precision for Industry 4.0 automation without new hardware.

Keywords:

Autonomous Guided Vehicles dead reckoning docking algorithm fuzzy logic

More Related Videos

Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Related Experiment Videos

Last Updated: Jan 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

Robotics and Automation
Control Systems Engineering
Industrial Informatics

Background:

Accurate docking of Autonomous Guided Vehicles (AGVs) is crucial for Industry 4.0 automated production systems.
Collaborative tasks with robotic arms require high AGV positioning precision.
Traditional odometry systems suffer from cumulative errors, becoming unacceptable during final docking.

Purpose of the Study:

To introduce a cost-effective software-upgrade solution for enhancing AGV docking precision.
To improve the final docking phase accuracy without necessitating new hardware.
To address the limitations of global navigation and odometry in critical docking scenarios.

Main Methods:

A two-stage navigation strategy: switching from global dead reckoning to a local scheme near the docking station.
Implementation of a Takagi-Sugeno Fuzzy Logic Controller (FLC) for precise final positioning.
Utilizing a gain-scheduling lookup table (LUT) within the FLC to synthesize heading and distance errors from proximity sensor data.

Main Results:

The fuzzy logic controller successfully guided AGVs to their final positions with high accuracy.
The LUT-based FLC robustly handled positional uncertainty and environmental variations.
Experimental validation demonstrated significant improvements in repeatable docking accuracy within industrial tolerances.

Conclusions:

The proposed software-upgrade solution effectively enhances AGV docking precision.
The novel FLC approach offers a cost-effective method for improving automated production systems.
This method overcomes the limitations of traditional odometry for critical docking tasks.