A photogrammetry-based error compensation solution, comprising calibration, positioning compensation and accuracy validation methodologies, is presented to the aerospace sector, able to assist industrial robots in manufacturing new composite materials, offering versatility and reconfigurability at a lower cost than that resulting from the currently used milling machines. Against a ground truth measured by a laser tracker, it has boosted, in real time, the accuracy level from ±0.685 to ±0.203 mm, on average, and from ±1.621 to ±0.498 mm at peak, following the ISO 9283 standard, and from ±0.534 to ±0.080 mm, on average, and from ±1.804 to ±0.456 mm at peak, with a real part in a large volume under industrial operating conditions, taking into account occlusions and showing robustness against the impact of the payload, the waviness, and the backlash.

The increasing market demands are driving companies to develop intelligent robotic cells capable of making autonomous decisions based on their environment. This work addresses the industrial challenge of distinguishing and tracking marked parts throughout the production process, where precision in identifying these parts and their marks poses a significant obstacle. As a solution, the integration of advanced learning techniques into a vision system for pick and place applications with a parallel delta robot is proposed. The developed system uses cubes with serigraphed letters on their top surface, identified through artificial vision to form words and control the process via a graphical interface. To automate letter recognition, an intelligent model based on a Convolutional Neural Network has been implemented, achieving 100% accuracy. Keywords: Delta Parallel Robot, Artificial Vision, Automation, Neural Networks, Machine Learning.