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

Product Description 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.  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.  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,