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Path Clustering with Homology Area
KTH. KTH, CAS, RPL, Royal Inst Technol, Stocholm, Sweden..
CUNY Coll Staten Isl, Math Dept, Staten Isl, NY 10314 USA.;CUNY, Grad Ctr, Comp Sci, New York, NY USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, CAS, RPL, Royal Inst Technol, Stocholm, Sweden..ORCID iD: 0000-0003-2965-2953
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, CAS, RPL, Royal Inst Technol, Stocholm, Sweden..ORCID iD: 0000-0003-1114-6040
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 7346-7353Conference paper, Published paper (Refereed)
Abstract [en]

Path clustering has found many applications in recent years. Common approaches to this problem use aggregates of the distances between points to provide a measure of dissimilarity between paths which do not satisfy the triangle inequality. Furthermore, they do not take into account the topology of the space where the paths are embedded. To tackle this, we extend previous work in path clustering with relative homology, by employing minimum homology area as a measure of distance between homologous paths in a triangulated mesh. Further, we show that the resulting distance satisfies the triangle inequality, and how we can exploit the properties of homology to reduce the amount of pairwise distance calculations necessary to cluster a set of paths. We further compare the output of our algorithm with that of DTW on a toy dataset of paths, as well as on a dataset of real-world paths.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 7346-7353
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Geometry
Identifiers
URN: urn:nbn:se:kth:diva-237170ISI: 000446394505086ISBN: 978-1-5386-3081-5 (print)OAI: oai:DiVA.org:kth-237170DiVA, id: diva2:1258256
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2020-02-18Bibliographically approved
In thesis
1. Topological Methods for Motion Prediction and Caging
Open this publication in new window or tab >>Topological Methods for Motion Prediction and Caging
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To fulfill the requirements of automation in unstructured environmentsit will be necessary to endow robots with the ability to plan actions thatcan handle the dynamic nature of changing environments and are robust toperceptual errors. This thesis focuses on the design of algorithms to facilitatemotion planning in human environments and rigid object manipulation.Understanding human motion is a necessary first step to be able to performmotion planning in spaces that are inhabited by humans. Specifically throughlong-term prediction a robot should be able to plan collision-avoiding paths tocarry out whatever tasks are required of it. In this thesis we present a methodto classify motions by clustering paths, together with a method to translatethe resulting clusters into motion patterns that can be used to predict motion.Another challenge of robotics is the manipulation of everyday objects.Even in the realm of rigid objects, safe object-manipulation by either grippersor dexterous robotic hands requires complex physical parameter estimation.Such estimations are often error-prone and misestimations may cause completefailure to execute the desired task. Caging is presented as an alternativeapproach to classical manipulation by employing topological invariants todetermine whether an object is secured with only bounded mobility. Wepresent a method to decide whether a rigid object is in fact caged by a givengrasp or not, relying only on a rough approximation of the object and thegripper.

Abstract [sv]

För att uppfylla kraven för automatisering i ostrukturerade miljöer ärdet nödvändigt att förse robotar med förmågan att planera i föränderligamiljöer. Denna avhandling fokuserar på design av algoritmer för att underlättarörelseplanering i mänskliga miljöer och manipulering av rigida objekt.För att planera handlingar i utrymmen där människor rör sig är detnödvändigt, som ett första steg, att förstå hur människor rör sig. Genom långsiktigaprognoser om människors rörelser kan en robot planera undvikande avkollisioner, samtidigt som en given uppgift kan planeras. Den här avhandlingenpresenterar både metoder för klassificering av rörelser, samt metoder för attanvända dessa klasser för förutsägelse av rörelser.En annan stor utmaning för robotik är manipulering av vardagliga objekt.För att manipulera rigida objekt med enkla gripdon, så väl som avanceraderobothänder, är det nödvändigt att uppskatta komplexa fysiska parametrar.Sådana uppskattningar innehar ofta fel som kan leda till misslyckande med attutföra den givna uppgiften. Caging är ett alternativ till klassisk manipulering,där topologiska invarianter används för att avgöra om ett objekt är säkratmed endast begränsad rörlighet. Vi presenterar en metod för att bestämmaom en konfiguration kan hålla ett objekt fast eller inte, som bara förlitar sigpå en förenklad modell av objekt och gripdon.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2020
Series
TRITA-EECS-AVL ; 2020:11
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-268370 (URN)978-91-7873-450-4 (ISBN)
Public defence
2020-03-17, F3, Lindstedtsvägen 26, 114 28 Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20200221

Available from: 2020-02-21 Created: 2020-02-18 Last updated: 2020-02-26Bibliographically approved

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Kragic, DanicaPokorny, Florian T.

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