The Next Step in Robot Commissioning: Autonomous Picking and Palletizing
Open accessAbstract
So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 at a success rate of 94.7. Our system is able to autonomously carry out simple order picking tasks in a human-safe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.
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Depth estimation, pose estimation, VO/VIO, and SLAM constitute the important 3D perceiving ability of AIoT and could be very useful in smart transportation, smart industry, smart agriculture, smart cities, and smart homes.
“Depth estimation, pose estimation, VO/VIO, and SLAM constitute the important 3D perceiving ability of AIoT, which could be very useful in smart transportation [137], smart industry [138], smart agriculture [139], [140], [141], smart cities and homes [142], [143], [144].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [138]
Krug et al. propose a novel grasp representation scheme allowing redundancy in the gripper pose placement to increase the safety, speed, and accuracy of autonomous picking and palletizing.
“To increase the safety, speed, and accuracy of autonomous picking and palletizing, Krug et al. propose a novel grasp representation scheme allowing redundancy in the gripper pose placement [138].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [138]
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Communication Protocol Design and Realization of Palletizing Robot
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Intelligent Depalletizing System Utilizing 3D Image Recognition and Industrial Robot for Camshaft
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Path Planning and Positioning Technology of Cotton Picking Robot in Complex Cotton Field
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AI supported Multi-Functional Gripping System for Dexterous Manipulation Tasks
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Robotic Depalletizing via Reinforcement Learning of a Pushing Policy
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- Authors
- Krug, R., Stoyanov, T., Tincani, V., Andreasson, H., Mosberger, R., Fantoni, G., & Lilienthal, A. J.
- Affiliations
- Örebro University (SE)
- University of Pisa (IT)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2016-01
- Volume
- 1
- Issue
- 1
- Pages
- 546-553
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2294174507
- Cited by
- 83
- References
- 27
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 6 · 2023: 8 · 2022: 17 · 2021: 9 · 2020: 10 · 2019: 4 · 2018: 12 · 2017: 5 · 2016: 1
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- Örebro University Library (Örebro University) (submittedVersion, other-oa)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Funders
- EU’s Seventh Framework Programme (ICT-270350)
- Swedish Knowledge Foundation (20140220)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Mechanisms and Dynamics
- Keywords
- palletizing, warehouse automation, research platform
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