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Autonomous object tracking with vision based control using a 2DOF robotic arm.

Umesh Kumar Sahu1, Mebin K S1, Abhinav K1

  • 1Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

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Summary

This study introduces a deep learning-based system for precise, real-time object tracking using a 2-DOF robotic arm. The vision-based control enhances autonomous capabilities for various applications, improving accuracy and response time.

Keywords:
2-DOF robotic armDeep learningFeature based trackingImage-based visual servoingObject tracking

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Real-time object tracking with robotic manipulators is crucial for applications like manufacturing and healthcare.
  • Traditional systems face challenges in sensor surveillance, stability, and complexity.
  • Existing visual servoing approaches have limitations that this study aims to overcome.

Purpose of the Study:

  • To design a precise and responsive object-tracking system for robotic arms.
  • To eliminate complexities associated with traditional tracking mechanisms.
  • To develop an autonomous vision-based control system for moving object tracking.

Main Methods:

  • An image-based visual servoing (IBVS) approach was employed for a 2-degree-of-freedom (DOF) robotic arm.
  • A deep learning-based object detection framework was utilized for real-time object identification and localization.
  • A vision-based control technique was designed, integrating the object detection system's real-time response.

Main Results:

  • The proposed deep learning controller demonstrated high accuracy and rapid response times in visual servoing tasks.
  • Simulations using CoppeliaSim and experimental validation with a 2-DOF robotic arm confirmed the strategy's effectiveness.
  • The system successfully tracked moving objects autonomously.

Conclusions:

  • The developed deep learning-based vision control strategy offers a robust solution for robotic arm object tracking.
  • The approach simplifies traditional methods by reducing reliance on complex mechanisms and multiple sensors.
  • Further exploration into data-driven learning techniques can enhance the control scheme's adaptability and robustness.