abstract
- This study presents the design and validation of a novel sampled-time state feedback controller for a multi-robotic system composed of a Cartesian robot and a Delta robot, providing five degrees of freedom¿two from the Cartesian and three from the Delta robot. The novelty of this work lies in the integration of a machine learning-based visual servoing strategy with the discrete-time control design, where the controller's sampling period is carefully selected based on the computational demands of the vision-based feedback. The end-effector's pose is estimated in real time using a modified eye-to-hand YOLOv8 algorithm embedded within a neural network visual servoing system. This approach enables the robotic system to continuously update its control actions based on visual feedback, enhancing trajectory tracking accuracy during task execution. A key contribution of this work is the investigation of the impact of sampling time on the convergence quality of the end-effector's tracking error. The proposed control architecture was rigorously evaluated in a virtual environment, where the end-effector pose estimated by the visual system was compared against ground truth data. The experimental results demonstrate that adjusting the sampling time significantly improves control accuracy, thereby justifying the proposed discrete-time controller design. This integrated machine learning and sampled-time control approach shows promising potential for real-time, vision-guided robotic manipulation. Highlights A synthetic database for the proposed controller using the Segment Anything Model (SAM) for image processing. A CNN algorithm to detect the position and orientation of the end-effector of a composite robotic manipulator based on a sequential configuration of an open-closed kinematic chain. A sampled controller that drives the position/orientation of the composite robotic manipulator to time-varying specific target configurations, including its convergence analysis. © 2025 Informa UK Limited, trading as Taylor & Francis Group.