Abstract
In recent years, logistics costs in the automotive industry have risen significantly. One way to reduce these costs is to automate the entire material flow. To meet the flexible industrial challenges and dynamic changes, robots with intelligent perception are necessary. Such a perception algorithm is presented in the following. It consists of three modules. In the first module, all objects in the field of vision of the robot are detected, and their position is determined. Then the relevant objects for the respective process are selected. Finally, the gripping point of the next object to be handled is determined. By integrating the robots, it can be shown that by combining intelligent modules with pragmatic frame modules, automation in a challenging industrial environment is feasible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
K. Permenter, Cost and Complexity: Top Challenges of Today’s Chief Supply Chain Officer (Aberdeen Group, 2012)
A. Stringflow, How to Reduce Logistics Costs: 19 Experts Reveal Ways Organizations Can Cut Their Logistics Transportation and Carry Costs (New Technology, 2018)
D. Arnold et al., Handbuch Logistik (Springer, Berlin, 2008), ISBN: 978-3-540-72929-7
R. Hodson, How robots are grasping the art of gripping. Nature 557, 23–25 (2018)
D. Holz, Registration with the point cloud library. IEEE Robot. Autom. Mag. 22(4), 110–124 (2015)
A. Romea et al., Object recognition and full pose registration from a single image for robotic manipulation, in ICRA (2009)
M. Dogar et al., Physics-based grasp planning through clutter, in RSS (2012)
I. Lenz et al., Deep learning for detecting robotic grasps, in ICLR (2013)
A. ten Pas et al., Grasp pose detection in point clouds (2017). arXiv: 1706.09911v1
W. Kehl et al. SSD-6D: making RGB-based 3D detection and 6D pose estimation great again, in Proceedings of the IEEE International Conference on Computer Vision (2017)
F. Spenrath et al., Gripping point determination for bin picking using heuristic search. Procedia CIRP 62, 606–611 (2017). https://doi.org/10.1016/j.procir.2016.06.015
R. Bostelman et al., Survey of industrial manipulation technologies for autonomous assembly applications. NIST-Internal Report 7844 (2012)
M. Robertson et al., New soft robots really suck: vacuum-powered systems empower diverse capabilities. Sci. Robot. 2(9) (2017)
S. Gu et al., Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates, in 2017 IEEE International Conference on Robotics and Automation (ICRA) (2017). IEEE
L. Fanet al., SURREAL: open-source reinforcement learning framework and robot manipulation benchmark, in Conference on Robot Learning (2018)
J. Mahler et al., Dex-Net 3.0: computing robust robot vacuum suction grasp targets in point clouds using a new analytic model and deep learning (2017). arXiv:1709.06670
A. Zeng et al., Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge, in Robotics and Automation (ICRA) (2017)
S. Kai-Tai et al., CAD-based pose estimation design for random bin picking using a RGB-D camera. J. Intell. Robot. Syst. 87(3–4), 455–470 (2017)
J. Tremblay et al., Falling things: a synthetic dataset for 3D object detection and pose estimation (2018). arXiv:1804.06534
H. Hiteshree et al., Analysis of feature based object mining and tagging algorithm considering different levels of occlusion, in Communication and Signal Processing (ICCSP) (2017)
S. Sundararajan et al., Continuous set of image processing methodology for efficient image retrieval using BOW SHIFT and SURF features for emerging image processing applications, in 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy) (2017)
W. Li et al., Visual recognition in RGB images and videos by learning from RGB-D data. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 2030–2036 (2018)
R. Shaoqing et al., Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1137–1149 (2018)
R. Shaoqing et al., Object detection networks on convolutional feature maps. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1476–1481 (2017)
J. Redmon, A. Farhadi, Yolov3: an incremental improvement. arXiv:1804.02767
Liu, Wei, et al. Ssd: Single shot multibox detector, in European Conference on Computer Vision (Springer, Cham, 2016), pp. 21–37
R. Girshick, Fast r-cnn, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1440–1448
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Poss, C., Irrenhauser, T., Prueglmeier, M., Goehring, D., Salehi, V., Zoghlami, F. (2020). Enabling Robust and Autonomous Materialhandling in Logistics Through Applied Deep Learning Algorithms. In: Wani, M., Kantardzic, M., Sayed-Mouchaweh, M. (eds) Deep Learning Applications. Advances in Intelligent Systems and Computing, vol 1098. Springer, Singapore. https://doi.org/10.1007/978-981-15-1816-4_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-1816-4_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1815-7
Online ISBN: 978-981-15-1816-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)