Abstract
Within the concept of “Industry 4.0”, one of the fundamental pillars is the concept of intelligent manufacturing. This type of manufacturing demands a high level of adaptability to design changes, greater flexibility in the adjustment of processes and an intensive use of digital information to improve them, being advanced robotics one of the key technologies to achieve this goal.
Classical industrial robotics is evolving towards another production model, which demands the rapid reconfiguration of robotic installations to manufacture different and varied products in smaller batches. In a production environment where flexibility and readjustment to the manufacture of new products must be carried out quickly, one of the fundamental tasks to be accomplished in robotics to reach these objectives efficiently is Bin Picking.
The problem of Bin Picking is one of the basic problems in artificial vision applied to robotics, and although there are numerous research studies related to this problem, it is difficult to seek the adaptability of the solutions provided to a real environment.
The present research work presents a new procedure for the solution of the Bin Picking problem, of quick configuration and execution based on artificial intelligence and point cloud processing.
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Iglesias, A.T., Pastor-López, I., Urquijo, B.S., García-Bringas, P. (2020). Effective Bin Picking Approach by Combining Deep Learning and Point Cloud Processing Techniques. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_44
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