Yan, Z. orcid.org/0000-0002-0393-8665, Crombez, N., Buisson, J. et al. (3 more authors) (2021) A quantifiable stratification strategy for tidy-up in service robotics. In: Proceedings of 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO). 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 08-10 Jul 2021, Virtual conference (Tokoname, Japan). IEEE (Institute of Electrical and Electronics Engineers) , pp. 182-187. ISBN 9781665449540
Abstract
This paper addresses the problem of tidying up a living room in a messy condition with a service robot (i.e. domestic mobile manipulator). One of the key issues in completing such a task is how to continuously select the object to grasp and take it to the delivery area, especially when the robot works in constrained and partially observable environments. In this paper, we propose a quantifiable stratification method that allows the robot to find feasible action plans according to different configurations of objects-deposits, in order to smoothly deliver the objects to the target deposits. Specifically, it leverages a finite-state machine obeying the principle of Occam's razor (called O- FSM), which is designed to integrate arbitrary user-defined action plans typically ranging from simple to complex. Instead of considering a sophisticated model for the ever-changing objects-deposits configuration in the tidy-up task, we empower the robot to make simple yet effective decisions based on its current faced configuration under a generalized framework. Through scenario planning and simulation experiments with the explicitly designed test cases based on the real robot and the real competition scene, the effectiveness of our method is illustrated.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number The Royal Society RGS\R2\202432 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jun 2021 08:27 |
Last Modified: | 28 Sep 2022 00:15 |
Status: | Published |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Refereed: | Yes |
Identification Number: | 10.1109/ARSO51874.2021.9542842 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:174430 |