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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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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...
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One-Degree-of-Freedom System01:24

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Buoyancy and Stability for Submerged and Floating Bodies01:11

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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Three-Dimensional Force System:Problem Solving01:30

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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.
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
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Related Experiment Video

Updated: Dec 29, 2025

Dynamic Navigation for Dental Implant Placement
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Neuro-Fuzzy Dynamic Position Prediction for Autonomous Work-Class ROV Docking.

Petar Trslić1, Edin Omerdic1, Gerard Dooly1

  • 1Centre for Robotics & Intelligent Systems, University of Limerick, Limerick V94 T9PX, Ireland.

Sensors (Basel, Switzerland)
|February 5, 2020
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Summary

This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict docking station heave motion for remotely operated vehicles (ROVs). This method enables more accurate autonomous underwater docking by overcoming ROV limitations.

Keywords:
ANFISPosition predictionROV docking

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Area of Science:

  • Robotics and Autonomous Systems
  • Ocean Engineering
  • Artificial Intelligence

Background:

  • Remotely Operated Vehicles (ROVs) face challenges matching surface vessel heave motion due to power, drag, and inertia limitations.
  • Current ROV docking relies on manual pilot control, which is unsuitable for autonomous operations.
  • Accurate prediction of docking station heave motion is crucial for dynamic underwater docking maneuvers.

Purpose of the Study:

  • To develop and present an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based method for predicting docking station heave motion.
  • To enable more reliable and accurate autonomous docking for work-class ROVs.
  • To provide a solution for the limitations of human-in-the-loop control in dynamic ROV docking.

Main Methods:

  • Implementation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for heave motion prediction.
  • Utilizing real-world trajectory data recorded during offshore trials in the North Atlantic Ocean.
  • Testing the ANFIS model with data from a work-class ROV and a cage type Tether Management System (TMS).

Main Results:

  • The ANFIS-based method demonstrated effective prediction of docking station heave motion.
  • The system's performance was validated using actual offshore trial data.
  • The proposed method shows promise for enhancing autonomous docking capabilities.

Conclusions:

  • The ANFIS approach offers a viable solution for predicting docking station heave motion, crucial for autonomous ROV operations.
  • This predictive capability can significantly improve the safety and efficiency of dynamic underwater docking.
  • The study highlights the potential of AI-driven methods in subsea robotics and autonomous systems.