Closed-loop pallet manipulation in unstructured environments
This paper addresses the problem of autonomous manipulation of a priori unknown palletized cargo with a robotic lift truck (forklift). Specifically, we describe coupled perception and control algorithms that enable the vehicle to engage and place loaded pallets relative to locations on the ground or...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers / Robotics Society of Japan
2011
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| Online Access: | http://hdl.handle.net/1721.1/63145 https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 |
| _version_ | 1811078394398375936 |
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| author | Walter, Matthew R. Karaman, Sertac Frazzoli, Emilio Teller, Seth |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Walter, Matthew R. Karaman, Sertac Frazzoli, Emilio Teller, Seth |
| author_sort | Walter, Matthew R. |
| collection | MIT |
| description | This paper addresses the problem of autonomous manipulation of a priori unknown palletized cargo with a robotic lift truck (forklift). Specifically, we describe coupled perception and control algorithms that enable the vehicle to engage and place loaded pallets relative to locations on the ground or truck beds. Having little prior knowledge of the objects with which the vehicle is to interact, we present an estimation framework that utilizes a series of classifiers to infer the objects' structure and pose from individual LIDAR scans. The classifiers share a low-level shape estimation algorithm that uses linear programming to robustly segment input data into sets of weak candidate features. We present and analyze the performance of the segmentation method, and subsequently describe its role in our estimation algorithm. We then evaluate the performance of a motion controller that, given an estimate of a pallet's pose, is employed to safely engage each pallet. We conclude with a validation of our algorithms for a set of real-world pallet and truck interactions. |
| first_indexed | 2024-09-23T10:58:31Z |
| format | Article |
| id | mit-1721.1/63145 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:58:31Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers / Robotics Society of Japan |
| record_format | dspace |
| spelling | mit-1721.1/631452022-09-27T16:18:06Z Closed-loop pallet manipulation in unstructured environments Walter, Matthew R. Karaman, Sertac Frazzoli, Emilio Teller, Seth Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Teller, Seth Walter, Matthew R. Karaman, Sertac Frazzoli, Emilio Teller, Seth This paper addresses the problem of autonomous manipulation of a priori unknown palletized cargo with a robotic lift truck (forklift). Specifically, we describe coupled perception and control algorithms that enable the vehicle to engage and place loaded pallets relative to locations on the ground or truck beds. Having little prior knowledge of the objects with which the vehicle is to interact, we present an estimation framework that utilizes a series of classifiers to infer the objects' structure and pose from individual LIDAR scans. The classifiers share a low-level shape estimation algorithm that uses linear programming to robustly segment input data into sets of weak candidate features. We present and analyze the performance of the segmentation method, and subsequently describe its role in our estimation algorithm. We then evaluate the performance of a motion controller that, given an estimate of a pallet's pose, is employed to safely engage each pallet. We conclude with a validation of our algorithms for a set of real-world pallet and truck interactions. United States. Dept. of the Air Force (Air Force Contract FA8721-05-C-00) 2011-05-31T17:45:08Z 2011-05-31T17:45:08Z 2010-10 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-6674-0 2153-0858 INSPEC Accession Number: 11689778 http://hdl.handle.net/1721.1/63145 Walter, M.R. et al. “Closed-loop pallet manipulation in unstructured environments.” Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. 2010. 5119-5126. ©2010 IEEE https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 en_US http://dx.doi.org/10.1109/IROS.2010.5652377 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers / Robotics Society of Japan IEEE |
| spellingShingle | Walter, Matthew R. Karaman, Sertac Frazzoli, Emilio Teller, Seth Closed-loop pallet manipulation in unstructured environments |
| title | Closed-loop pallet manipulation in unstructured environments |
| title_full | Closed-loop pallet manipulation in unstructured environments |
| title_fullStr | Closed-loop pallet manipulation in unstructured environments |
| title_full_unstemmed | Closed-loop pallet manipulation in unstructured environments |
| title_short | Closed-loop pallet manipulation in unstructured environments |
| title_sort | closed loop pallet manipulation in unstructured environments |
| url | http://hdl.handle.net/1721.1/63145 https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 |
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