Robotic Pick-and-Place Time Optimization: Application to Footwear Production
JORGE BORRELL MÉNDEZ, CARLOS PEREZ-VIDAL,
JOSÉ VICENTE SEGURA HERAS, AND JUAN JOSÉ PÉREZ-HERNÁNDEZ
IEEE Access (03/12/2020)
Ed. IEEE DOI:10.1109/ACCESS.2020.3037145 - Vol. 8
This article considers the problem of optimizing the task sequences carried out by a dual-arm manipulator robot in a footwear production setting. The robot has to identify the pieces of a shoe put in a tray and pick-and-place them in a shoe mould for further processing. The shoe pieces arrive on a tray in random positions (patterns) and can be picked up in different order. In such a setting, a decision tree model is developed to recognize the pattern and predict the optimal sequence for picking the pieces up, thus, the picking and decision-making time is minimized. Two shoe models are considered for training and validating the solution proposed and the developed algorithm is applied in the real setting. There are not many studies which use the decision trees in sequencing and scheduling problems in robotics. The findings of this article show that the decision tree method has advantages in task planning in a complex environment consisting of multiple trajectories and possible collisions between robot arms.