Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché

Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots - Achim J. Lilienthal

Format: Broché

Note : 0

Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

Filtrer par :

179,45 €

Produit Neuf

Ou 44,86 € /mois

Livraison : 3,99 €
Livré entre le 30 juillet et le 5 août

Voir les modes de livraison

PRO Vendeur favori

4,8/5 sur + de 1 000 ventes

Voir le détail de l'annonce

Nos autres offres

194,86 €

Produit Neuf

Ou 48,72 € /mois

En savoir plus
- Livraison à 0,01 €
- Livré entre le 31 juillet et le 12 août
Voir les modes de livraison
RiaChristie

PRO

4,9/5 sur + de 1 000 ventes

Contacter le vendeur

Afficher la description de l'annonce

Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9783030418106_dbm

Voir le détail de l'annonce

Vous avez choisi le retrait chez le vendeur à

Payez directement sur Rakuten (CB, PayPal, 4xCB...)
Récupérez le produit directement chez le vendeur
Rakuten vous rembourse en cas de problème

Terminer ma commande

Gratuit et sans engagement

Félicitations !

Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

TROUVER UN MAGASIN

Adresse

Retour

Horaires

Note :

Avis sur Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché - Livre Littérature Générale

Note : 0 0 avis sur Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché - Livre Littérature Générale

Donnez votre avis

Donnez votre avis sur ce produit

Votre note * : 1/5 2/5 3/5 4/5 5/5 Veuillez donner une note au produit

Le titre de votre avis* : Saisissez un titre

Quelques conseils :

Donnez le ton de votre avis, résumez-le en quelques mots qui apparaîtront comme le titre de votre avis.

Votre avis* : (40 caractères minimum) Saisissez un avis

Quelques conseils :

N'hésitez pas à mettre en avant les caractéristiques de ce produit, n'oubliez pas de mentionner ses avantages et/ou ses défauts éventuels. Décrivez précisément, argumentez, soyez convaincant !

Annuler

Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.

Produits similaires

Présentation Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché
- Livre Littérature Générale

Livre Littérature Générale - Achim J. Lilienthal - 01/03/2021 - Broché - Langue : Anglais

Auteur(s) : Achim J. Lilienthal - Chittaranjan Srinivas Swaminathan - Luigi Palmieri - Martin Magnusson - Tomasz Piotr Kucner

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 01/03/2021

Format : Moyen, de 350g à 1kg

Nombre de pages : 180

Expédition : 283

Dimensions : 23.5 x 15.5 x 1.1

ISBN : 9783030418106

Résumé :

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.

Biographie:

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 ?rebro 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 ?rebro 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 ?rebro 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 Arti?cial 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 T?bingen

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), ?rebro University,

Sweden. He received his M.Sc. degree in Computer Science from Uppsala University,

Sweden, in 2004 and Ph.D. degree from ?rebro 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

e?cient 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 ?rebro University, Sweden. He received his M.Sc. degree in Computer Science

from ?rebro 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, c...

Sommaire:

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.

...

Détails de conformité du produit

Consulter les détails de conformité de ce produit (

Personne responsable dans l'UE

)

Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots - Achim J. Lilienthal

Avis sur Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché - Livre Littérature Générale

Présentation Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché - Livre Littérature Générale

Présentation Probabilistic Mapping Of Spatial Motion Patterns For Mobile Robots de Achim J. Lilienthal Format Broché
- Livre Littérature Générale