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MARLIN: A Cloud Integrated Robotic Solution to Support Intralogistics in Retail

Published 2 Jul 2024 in cs.RO, cs.AI, and cs.CV | (2407.02078v1)

Abstract: In this paper, we present the service robot MARLIN and its integration with the K4R platform, a cloud system for complex AI applications in retail. At its core, this platform contains so-called semantic digital twins, a semantically annotated representation of the retail store. MARLIN continuously exchanges data with the K4R platform, improving the robot's capabilities in perception, autonomous navigation, and task planning. We exploit these capabilities in a retail intralogistics scenario, specifically by assisting store employees in stocking shelves. We demonstrate that MARLIN is able to update the digital representation of the retail store by detecting and classifying obstacles, autonomously planning and executing replenishment missions, adapting to unforeseen changes in the environment, and interacting with store employees. Experiments are conducted in simulation, in a laboratory environment, and in a real store. We also describe and evaluate a novel algorithm for autonomous navigation of articulated tractor-trailer systems. The algorithm outperforms the manufacturer's proprietary navigation approach and improves MARLIN's navigation capabilities in confined spaces.

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References (30)
  1. R. Bogue, Strong prospects for robots in retail, Industrial Robot: the international journal of robotics research and application 46 (2019). doi:10.1108/IR-01-2019-0023.
  2. B. Santra, D. P. Mukherjee, A comprehensive survey on computer vision based approaches for automatic identification of products in retail store, Image and Vision Computing 86 (2019) 45–63. URL: https://www.sciencedirect.com/science/article/pii/S0262885619300277. doi:https://doi.org/10.1016/j.imavis.2019.03.005.
  3. Remote retail monitoring and stock assessment using mobile robots, in: 2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), 2014, pp. 1–6. doi:10.1109/TePRA.2014.6869136.
  4. Robotic retail surveying by deep learning visual and textual data, Robotics and Autonomous Systems 118 (2019) 179–188. doi:https://doi.org/10.1016/j.robot.2019.01.021.
  5. Robots collecting data: Modelling stores, in: Robotics for Intralogistics in Supermarkets and Retail Stores, Springer, 2022, pp. 41–64.
  6. Digital Twin: Origin to Future, Applied System Innovation 4 (2021). URL: https://www.mdpi.com/2571-5577/4/2/36. doi:10.3390/asi4020036.
  7. Semantic 3d object maps for everyday robotic retail inspection, in: M. Cristani, A. Prati, O. Lanz, S. Messelodi, N. Sebe (Eds.), New Trends in Image Analysis and Processing – ICIAP 2019, Springer International Publishing, Cham, 2019, pp. 263–274.
  8. Know rob 2.0—a 2nd generation knowledge processing framework for cognition-enabled robotic agents, in: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018, pp. 512–519.
  9. Navigation of autonomous tractor for orchards and plantations using a laser range finder: Automatic control of trailer position with tractor, Biosystems Engineering 147 (2016) 90–103. URL: https://www.sciencedirect.com/science/article/pii/S1537511015302439. doi:https://doi.org/10.1016/j.biosystemseng.2016.02.009.
  10. Navigation system for agricultural machines: Nonlinear model predictive path tracking, Computers and Electronics in Agriculture 82 (2012) 32–43. URL: https://www.sciencedirect.com/science/article/pii/S0168169911003218. doi:https://doi.org/10.1016/j.compag.2011.12.009.
  11. Optimization-Based On-Road Path Planning for Articulated Vehicles**This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation., IFAC-PapersOnLine 53 (2020) 15572–15579. URL: https://www.sciencedirect.com/science/article/pii/S2405896320330846. doi:https://doi.org/10.1016/j.ifacol.2020.12.2402.
  12. Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach, IEEE Transactions on Intelligent Vehicles PP (2019) 1–1. doi:10.1109/TIV.2019.2960943.
  13. Real-Time Trajectory Planning for On-road Autonomous Tractor-Trailer Vehicles, Journal of Shanghai Jiaotong University(Science) 26 (2021) 722–730.
  14. Learning and executing generalized robot plans, Artificial Intelligence 3 (1972) 251–288. URL: https://www.sciencedirect.com/science/article/pii/0004370272900513. doi:https://doi.org/10.1016/0004-3702(72)90051-3.
  15. Towards performing everyday manipulation activities, Robotics and Autonomous Systems 58 (2010) 1085–1095. URL: https://www.sciencedirect.com/science/article/pii/S0921889010001119. doi:https://doi.org/10.1016/j.robot.2010.05.007, hybrid Control for Autonomous Systems.
  16. Planning with a task modeling framework in manufacturing robotics, in: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 5787–5794. doi:10.1109/IROS.2013.6697194.
  17. Rosplan: Planning in the robot operating system, Proceedings of the International Conference on Automated Planning and Scheduling 25 (2015) 333–341. URL: https://ojs.aaai.org/index.php/ICAPS/article/view/13699. doi:10.1609/icaps.v25i1.13699.
  18. A. Bolu, O. Korçak, Adaptive task planning for multi-robot smart warehouse, IEEE Access 9 (2021) 27346–27358. doi:10.1109/ACCESS.2021.3058190.
  19. Adaptive task planning for large-scale robotized warehouses, in: 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022, pp. 3327–3339. doi:10.1109/ICDE53745.2022.00314.
  20. Multimodal sensor-based whole-body control for human–robot collaboration in industrial settings, Robotics and Autonomous Systems 94 (2017) 102–119. URL: https://www.sciencedirect.com/science/article/pii/S0921889016305127. doi:https://doi.org/10.1016/j.robot.2017.04.007.
  21. R. B. Rusu, S. Cousins, 3D is here: Point Cloud Library (PCL), in: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011.
  22. Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830.
  23. The office marathon: Robust navigation in an indoor office environment, in: 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 300–307. doi:10.1109/ROBOT.2010.5509725.
  24. Trajectory modification considering dynamic constraints of autonomous robots, in: ROBOTIK 2012; 7th German Conference on Robotics, 2012, pp. 1–6.
  25. Retail semdt collection knowledge-base, a platform architecture, 2023 IEEE 3nd International Conference on Digital Twins and Parallel Intelligence (DTPI) (2023). Accepted for publication.
  26. Rosplan: Planning in the robot operating system, in: Proceedings of the international conference on automated planning and scheduling, volume 25, 2015, pp. 333–341.
  27. Forward-chaining partial-order planning, in: Proceedings of the International Conference on Automated Planning and Scheduling, volume 20, 2010, pp. 42–49.
  28. Pddl - the planning domain definition language (1998).
  29. A rosplan-based multi-robot navigation system, in: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), 2018, pp. 248–253. doi:10.1109/LARS/SBR/WRE.2018.00053.
  30. Experimental evaluation of agv dispatching methods in an agent-based simulation environment and a digital twin, Applied Sciences 13 (2023) 6171.
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