Closed-loop pallet manipulation in unstructured environments
Author(s)
Walter, Matthew R.; Karaman, Sertac; Frazzoli, Emilio; Teller, Seth
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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.
Date issued
2010-10Department
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 SystemsJournal
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers / Robotics Society of Japan
Citation
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
Version: Final published version
Other identifiers
INSPEC Accession Number: 11689778
ISBN
978-1-4244-6674-0
ISSN
2153-0858