Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots (eBook)

eBook Download: PDF
2020 | 1st ed. 2020
XXV, 151 Seiten
Springer International Publishing (Verlag)
978-3-030-41808-3 (ISBN)

Lese- und Medienproben

Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots - Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
Systemvoraussetzungen
117,69 inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen
This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.
The world around us is constantly changing. Nonetheless, we can find our way and aren't overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field. 



Tomasz Piotr Kucner received his B.Sc. in Computer Management Systems in
Manufacturing (2011) and M.Sc. in Robotics (2012) at Wroclaw University of Tech-
nology. In 2018, he received a tekn. dr. (Ph. D.) degree from Örebro University.
During his PhD studies he was part of KKS research project ALLO and EU FP7
research rpoject SPENCER. His work in these projects was focussed on building
spatial models of dynamics. Dr. Kucner currently works as Post-doctoral researcher
in the Mobile Robotics & Olfaction lab of AASS at Örebro University, Sweden. He is
mainly involved in the EU H2020 research project ILIAD, where he is working with
methods for automatic map quality assessment and building spatio-temporal models
of dynamics.

Achim J. Lilienthal is full professor of Computer Science at Örebro University
where he leads the Mobile Robotics and Olfaction (MRO) Lab. His core research
interests are perception systems in unconstrained, dynamic environments. Typically
based on approaches that leverage domain knowledge and Arti?cial Intelligence, his
research work addresses rich 3D perception and navigation of autonomous transport
robots, mobile robot olfaction, human robot interaction and mathematics education
research. Achim J. Lilienthal obtained his Ph.D. in computer science from Tübingen
University. The Ph.D. thesis addresses gas distribution mapping and gas source lo-
calisation with mobile robots. He has published more than 250 refereed conference
papers and journal articles and is senior member of IEEE.

Martin Magnusson is currently docent (associate professor) in Computer Science
at the Center of Applied Autonomous Sensor Systems (AASS), Örebro University,
Sweden. He received his M.Sc. degree in Computer Science from Uppsala University,
Sweden, in 2004 and Ph.D. degree from Örebro University in 2009. Dr. Magnusson
has been vice-chair of the working group for the IEEE/RAS standards for 2D and 3D
map representations and is deputy chair for the eu-Robotics topic group on robots
for logistics and transport. His research interests include 3D perception (including
e?cient and versatile 3D surface representations), creation and usage of robot maps
that go beyond mere geometry, and methods for making use of heterogeneous maps
with high uncertainty.

Luigi Palmieri is a research scientist at Robert Bosch GmbH - Corporate Re-
search. His research focuses currently on the topic of motion planning and control in
cluttered and dynamic environments for wheeled mobile robotics, machine learning
and social-navigation. He earned his PhD degree in robot motion planning from the
University of Freiburg, Germany. During his PhD he was responsible for the motion
planning task of the EU FP7 project Spencer. He currently has the same responsi-
bility in the EU H2020 project ILIAD. He has co-authored multiple papers at RA-L,
ICRA, IROS, IJRR, FSR about combinations of motion planning with control, search,
machine learning and human motion prediction.

Chittaranjan Srinivas Swaminathan is a doctoral student in Computer Science
at Örebro University, Sweden. He received his M.Sc. degree in Computer Science
from Örebro University in June, 2017, and his Bachelor of Technology in Mecha-
tronics from SASTRA University, Thanjavur, India, in September, 2012. His interests
include motion planning, control and multi-agent coordination in dynamic environ-
ments. He is also involved in the software integration and motion planning tasks in
the EU H2020 project ILIAD.

Erscheint lt. Verlag 28.3.2020
Reihe/Serie Cognitive Systems Monographs
Zusatzinfo XXV, 151 p. 69 illus., 66 illus. in color.
Sprache englisch
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Bauwesen
Technik Maschinenbau
Schlagworte Autonomous Robots • Cognitive Systems • Mobile Robots • Probabilistic Mapping • robots
ISBN-10 3-030-41808-1 / 3030418081
ISBN-13 978-3-030-41808-3 / 9783030418083
Haben Sie eine Frage zum Produkt?
PDFPDF (Wasserzeichen)
Größe: 8,0 MB

DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasser­zeichen und ist damit für Sie persona­lisiert. Bei einer missbräuch­lichen Weiter­gabe des eBooks an Dritte ist eine Rück­ver­folgung an die Quelle möglich.

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seiten­layout eignet sich die PDF besonders für Fach­bücher mit Spalten, Tabellen und Abbild­ungen. Eine PDF kann auf fast allen Geräten ange­zeigt werden, ist aber für kleine Displays (Smart­phone, eReader) nur einge­schränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich
der Praxis-Guide für Künstliche Intelligenz in Unternehmen - Chancen …

von Thomas R. Köhler; Julia Finkeissen

eBook Download (2024)
Campus Verlag
38,99
Wie du KI richtig nutzt - schreiben, recherchieren, Bilder erstellen, …

von Rainer Hattenhauer

eBook Download (2023)
Rheinwerk Computing (Verlag)
24,90