Abstract
Distribution centers are facilities designed to store and manage different products to be redistributed to another location or directly to customers. Although these centers have a high degree of automation, tasks such as picking are hardly automatable and are one of the most labor-intensive and monotonous tasks in material handling operations. Picking, i.e. the process of collecting items to create a package for shipment, is the main source of errors and lack of efficiency. Although the fully automation of the picking task is highly desirable, multiple factors such as the high variability of parts or industrial requirements (safety, robustness...) limit the fully automation of the task. RSAII developed a hybrid order picking approach, in which robots and humans share the same workspace, combining high automation, flexibility and safety. The objectives defined in the project addressed key technical and industrial challenges and three experiments were performed with increasing complexity: (1) Free-style: Mono-reference picking with high availability, (2) Showcase: Safe multi-reference integrated with current pick to light solutions, (3) Field test: Collaborative solution for Unitary Picking in order preparation area of a Distribution Center. As a result of the Field test, it has been developed a working prototype that works in a realistic environment and represents the foundation for real implementation of the system at ULMA customers’ premises.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jonschkowski, R., Eppner, C., Höfer, S., Martín-Martín, R., Brock, O.: Probabilistic multi-class segmentation for the amazon picking challenge. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1–7 (Oct 2016)
Zhang, H., Long, P., Zhou, D., Qian, Z., Wang, Z., Wan, W., Chen, Y.: An autonomous picking system for general objects. In: 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 721–726 (Aug 2016)
Eppner, C., Höfer, S., Jonschkowski, R., Martin-Martin, R., Sieverling, A., Wall, V., Brock, O.: Lessons from the amazon picking challenge: four aspects of building robotic systems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 4831–4835 (2017)
Amazon robotics challenge.: https://www.amazonrobotics.com/#/roboticschallenge. Accessed 2017
Scallog system.: https://www.scallog.com/en/. Accessed 2017
Fetch robotics.: https://fetchrobotics.com/. Accessed 2017
Magazzino simple storage.: https://www.magazino.eu/?lang=en. Accessed 2017
Locusrobotics.: https://locusrobotics.com/. Accessed 2017
Swisslog autopiq.: https://www.swisslog.com/en-us/. Accessed 2017
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (November 1997)
Hochreiter, S., Bengio, Y., Frasconi, P.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kolen, J., Kremer, S. (eds.) Field Guide to Dynamical Recurrent Networks. IEEE Press (2001)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (March 1994)
Dósa, G.: The tight bound of first fit decreasing bin-packing algorithm is \(ffd(i) \le 11/9opt(i)+6/9\). In: Proceedings of the First International Conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies, ESCAPE’07, pp. 1–11. Springer, Berlin, Heidelberg (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Susperregi, L. et al. (2020). RSAII: Flexible Robotized Unitary Picking in Collaborative Environments for Order Preparation in Distribution Centers. In: Caccavale, F., Ott, C., Winkler, B., Taylor, Z. (eds) Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users. Springer Tracts in Advanced Robotics, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-34507-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-34507-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34506-8
Online ISBN: 978-3-030-34507-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)