Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: 40 (Cognitive Systems Monographs, 40) - Hardcover

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

Hardcover

ISBN 10: 3030418073 ISBN 13: 9783030418076

Publisher: Springer, 2020

Synopsis

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. 

About this title

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.

From the Back Cover

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. 

Edition details

• Publisher: Springer
• Publication date: 2020
• Language: English
• ISBN 10: 3030418073
• ISBN 13: 9783030418076
• Binding: Hardcover
• Edition number: 1
• Number of pages: 176