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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.
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Manipulating XXY Planar Platform Positioning Accuracy by Computer Vision Based on Reinforcement Learning.

Yi-Cheng Huang¹, Yung-Chun Chan²

¹Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This study uses machine learning and visual recognition to improve industrial automation. A reinforcement Q-learning algorithm accurately estimates positioning errors for enhanced precision motion control.

Keywords:

computer vision deep Q-learning network reinforcement learning

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Area of Science:

Robotics and Automation
Artificial Intelligence
Machine Learning

Background:

The increasing demand for industrial automation and precise control in Industry 4.0 necessitates advanced solutions.
Traditional methods for machine parameter tuning are costly and may not achieve high-precision positioning.
Factors like ball-screw clearance, backlash, and nonlinear friction impede accuracy and reproducibility in planar platforms.

Purpose of the Study:

To develop an intelligent system for high-precision positioning of an XXY planar platform.
To accurately determine and compensate for positioning errors using machine learning.
To enhance the accuracy and reproducibility of industrial automation control systems.

Main Methods:

A visual image recognition system captured platform displacement data.
A charge-coupled device camera provided real-time imaging.
A reinforcement Q-learning algorithm, utilizing time-differential learning and accumulated rewards, was employed for Q-value iteration and optimal positioning.

Main Results:

A deep Q-network model was successfully constructed and trained.
The model effectively estimated the XXY platform's positioning error.
The system predicted command compensation based on historical error data, validated through simulations.

Conclusions:

The developed deep Q-network model accurately estimates positioning errors in planar platforms.
Reinforcement learning offers a viable approach to enhance precision motion control in industrial automation.
This AI-driven methodology is extensible to various feedback control applications.