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The Repetition Roadmap for Repetitive Constrained Motion Planning

Lehner, Peter and Albu-Schäffer, Alin (2018) The Repetition Roadmap for Repetitive Constrained Motion Planning. IEEE Robotics and Automation Letters, 3 (4), pp. 3884-3891. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LRA.2018.2856925 ISSN 2377-3766

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Official URL: https://ieeexplore.ieee.org/document/8412538

Abstract

We present the Repetition Roadmap, a motion planner that effectively exploits the repetitiveness of a set of tasks with small variations to efficiently compute new motions. The method learns an abstract roadmap of probability distributions for the configuration space of a particular task set from previous solution paths. We show how to construct the Repetition Roadmap by learning a Gaussian mixture model and connecting the distribution components based on the connectivity information of the prior paths. We present an algorithm that exploits the information in the Repetition Roadmap to guide the search for solutions of similar tasks. We illustrate our method in a maze, which explains the construction of the Repetition Roadmap and how the method can generalize over different environments. We show how to apply the Repetition Roadmap to similar constrained manipulation tasks and present our results including significant speedup in computation time when compared to uniform and adaptive sampling.

Item URL in elib:https://elib.dlr.de/124823/
Document Type:Article
Title:The Repetition Roadmap for Repetitive Constrained Motion Planning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Lehner, PeterPeter.Lehner (at) dlr.dehttps://orcid.org/0000-0002-3755-1186
Albu-Schäffer, Alinalin.albu-schaeffer (at) dlr.dehttps://orcid.org/0000-0001-5343-9074
Date:October 2018
Journal or Publication Title:IEEE Robotics and Automation Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:3
DOI :10.1109/LRA.2018.2856925
Page Range:pp. 3884-3891
Editors:
EditorsEmail
Amato, Nancyamato@tamu.edu
Thomas, S.UNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2377-3766
Status:Published
Keywords:Task analysis, Planning, Manipulators, Service robots, Libraries, Gaussian mixture model
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben on Orbit Servicing
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Autonomy and Teleoperation
Deposited By: Lehner, Peter
Deposited On:16 Dec 2018 23:35
Last Modified:31 Jul 2019 20:22

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