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.
Almaliki et al. (Sat,) studied this question.