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  • Book
  • © 2020

Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots

Authors:

  1. Tomasz Piotr Kucner

    1. AASS Mobile Robotics & Olfaction Lab, Örebro University, Örebro, Sweden

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  2. Achim J. Lilienthal

    1. AASS Mobile Robotics & Olfaction Lab, Örebro University, Örebro, Sweden

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  3. Martin Magnusson

    1. AASS Mobile Robotics & Olfaction Lab, Örebro University, Örebro, Sweden

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  4. Luigi Palmieri

    1. Corporate Research, Robert Bosch GmbH, Renningen, Germany

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  5. Chittaranjan Srinivas Swaminathan

    1. AASS Mobile Robotics & Olfaction Lab, Örebro University, Örebro, Sweden

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  • Investigates recent methods that make it possible to represent the broad range of real-world spatial motion patterns in a compact, yet meaningful way

  • Primarily focuses on creating maps that capture the motion patterns of dynamic objects and/or the flows of continuous media

  • Presents recent research on probabilistic mapping of motion patterns for mobile robots

Part of the book series: Cognitive Systems Monographs (COSMOS, volume 40)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xxv
    PDF

  2. Introduction

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 1-13

  3. Maps of Dynamics

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 15-32

  4. Modelling Motion Patterns with Conditional Transition Map

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 33-64

  5. Modelling Motion Patterns with Circular-Linear Flow Field Maps

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 65-113

  6. Motion Planning Using MoDs

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 115-141

  7. Closing Remarks

    • Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan
    Pages 143-151
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About this book

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. 

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Keywords

  • Mobile Robots
  • Probabilistic Mapping
  • Autonomous Robots
  • Robots
  • Cognitive Systems
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Reviews

“The purpose of this book is to provide an idea for motion planning of robots realizing tasks in unknown environment and self-control their work. The book should be of interest to master and Ph.D. students as well as practicing and research engineers in the field of robotics.” (Clementina Mladenova, zbMATH 1478.93002, 2022)
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Authors and Affiliations

  • AASS Mobile Robotics & Olfaction Lab, Örebro University, Örebro, Sweden

    Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Chittaranjan Srinivas Swaminathan

  • Corporate Research, Robert Bosch GmbH, Renningen, Germany

    Luigi Palmieri

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About the authors

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 Artificial 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
efficient 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.

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Bibliographic Information

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Access via your institution
eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-41808-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 149.99
Price excludes VAT (USA)
Hardcover Book USD 149.99
Price excludes VAT (USA)
Learn about institutional subscriptions