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Probabilistic mapping of spatial mot...

Kucner, Tomasz Piotr.

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Probabilistic mapping of spatial motion patterns for mobile robots

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic mapping of spatial motion patterns for mobile robots/ by Tomasz Piotr Kucner ... [et al.].
其他作者:	Kucner, Tomasz Piotr.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xxv, 151 p. :ill., digital ;24 cm.
內容註:	Introduction -- Maps of Dynamics -- Modelling Motion Patterns with CT-Map -- Modelling Motion Patterns with CLiFF-Map -- Motion Planning using MoDs -- Closing Remarks.
Contained By:	Springer eBooks
標題:	Probabilities. -
電子資源:	https://doi.org/10.1007/978-3-030-41808-3
ISBN:	9783030418083

Probabilistic mapping of spatial motion patterns for mobile robots
Probabilistic mapping of spatial motion patterns for mobile robots[electronic resource] /by Tomasz Piotr Kucner ... [et al.]. - Cham :Springer International Publishing :2020. - xxv, 151 p. :ill., digital ;24 cm. - Cognitive systems monographs,v.401867-4925 ;. - Cognitive systems monographs ;v.40..

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

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.

ISBN: 9783030418083

Standard No.: 10.1007/978-3-030-41808-3doiSubjects--Topical Terms:

518889
Probabilities.

LC Class. No.: QA273

Dewey Class. No.: 519.2

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520 $a 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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