LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

Area of Science:

• Robotics and Automation
• Computer Vision
• Control Systems Engineering

Background:

• Visual servoing systems require high precision for tasks like LED chip placement.
• System inaccuracies and feedback delays can degrade positioning performance.
• Kalman filtering is a common approach, but standard methods struggle with dynamic uncertainties.

Purpose of the Study:

• To develop an advanced Kalman filter algorithm for improved LED chip positioning accuracy.
• To address and compensate for visual information delays in real-time systems.
• To enhance the robustness of visual servo systems against model parameter uncertainties.

Main Methods:

• Implementation of a dual rate Kalman filter structure within the visual servo control system.
• Integration of an adaptive forgetting factor to mitigate accumulated model errors.
• Compensation for visual information delay to ensure accurate time-sequential coordination.

Main Results:

• The proposed dual rate adaptive fading Kalman filter significantly reduced LED chip positioning errors.
• Experimental validation demonstrated the algorithm's effectiveness in improving accuracy.
• The method proved robust against inaccuracies and uncertainties in system model parameters.

Conclusions:

• The dual rate adaptive fading Kalman filter with delay compensation offers superior performance for LED chip visual servo positioning.
• This approach enhances system stability and accuracy, even with imperfect system models.
• The findings contribute to more precise robotic manipulation and automation in LED manufacturing.