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Area of Science:

  • Robotics and Control Systems
  • Computer Vision
  • Machine Learning

Background:

  • Rotor attitude detection (RAD) is crucial for controlling permanent magnet spherical motors (PMSpM).
  • Existing RAD methods may lack the real-time performance required for dynamic control applications.
  • Object detection algorithms offer a potential avenue for non-contact attitude estimation.

Purpose of the Study:

  • To develop an improved You Only Look Once v8n (YOLOv8n) based method for rotor attitude detection in PMSpM.
  • To enhance the real-time performance and accuracy of visual RAD for PMSpM.
  • To compare the proposed method against existing algorithms and a contact-based benchmark.

Main Methods:

  • Collection and annotation of custom visual image datasets for PMSpM.
  • Modification of the YOLOv8n architecture by integrating FasterNet and c2fFaster modules for improved real-time processing.
  • Training and validation of the enhanced YOLOv8n model using custom and public datasets (COCO 2017).
  • Comparison of the improved YOLOv8n method with the standard YOLOv8n and Multi-Object Kalman Kernel Correlation Filter (MKKCF) algorithms.

Main Results:

  • The improved YOLOv8n algorithm achieved an inference time as low as 4.31 ms on custom PMSpM datasets, ensuring high detection accuracy.
  • The enhanced algorithm demonstrated significantly superior performance compared to the original YOLOv8n and MKKCF algorithms.
  • The method enables calculation of PMSpM rotor attitude using three visual objects from a single monocular camera.

Conclusions:

  • The proposed improved YOLOv8n-based RAD method offers a highly accurate and real-time solution for PMSpM control.
  • The enhanced algorithm surpasses the precision of the MKKCF-based visual RAD method, as validated by spatial motion experiments.
  • This non-contact visual RAD approach provides a robust alternative for precise rotor attitude estimation in PMSpM.