Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots

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

Overview

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.

Product Details

ISBN-13:	9783030418076
Publisher:	Springer International Publishing
Publication date:	03/29/2020
Series:	Cognitive Systems Monographs , #40
Edition description:	1st ed. 2020
Pages:	151
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

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.

Table of Contents

Introduction.- Maps of Dynamics.- Modelling Motion Patterns with CT-Map.- Modelling Motion Patterns with CLiFF-Map.- Motion Planning using MoDs.- Closing Remarks.

Editorial 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)

From the Publisher