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<p>This research presents a novel approach to robotic manipulation by integrating an advanced machine learning-based object detection system on a resource-constrained AMB82-Mini microcontroller. Employing a lightweight, quantized YOLOv7-tiny model, the system achieves real-time object localization with high precision, enabling a 6-DoF robotic arm to perform complex pick-and-place tasks autonomously. The framework incorporates a machine learning-driven perception pipeline that interfaces with a kinematic solver to compute precise joint trajectories, enhanced by adaptive motion smoothing techniques. A closed-loop control system, augmented with sensor feedback, ensures robust performance across varying payloads. Experimental results validate the system’s efficacy, achieving consistent task success rates and computational efficiency on an embedded platform. This work demonstrates the potential of embedded machine learning to enable scalable, cost-effective automation solutions, offering insights into the synergy of perception and control in robotic systems.</p>

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Design and implementation of a 6-DoF robot arm control with object detection based on machine learning using mini microcontroller

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

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  • Hayder Hashim Almaliki,
  • Amir Hooshang Mazinan,
  • Seyed Mahmoud Modaresi

摘要

This research presents a novel approach to robotic manipulation by integrating an advanced machine learning-based object detection system on a resource-constrained AMB82-Mini microcontroller. Employing a lightweight, quantized YOLOv7-tiny model, the system achieves real-time object localization with high precision, enabling a 6-DoF robotic arm to perform complex pick-and-place tasks autonomously. The framework incorporates a machine learning-driven perception pipeline that interfaces with a kinematic solver to compute precise joint trajectories, enhanced by adaptive motion smoothing techniques. A closed-loop control system, augmented with sensor feedback, ensures robust performance across varying payloads. Experimental results validate the system’s efficacy, achieving consistent task success rates and computational efficiency on an embedded platform. This work demonstrates the potential of embedded machine learning to enable scalable, cost-effective automation solutions, offering insights into the synergy of perception and control in robotic systems.