Probabilistic mapping of spatial motion patterns for mobile robots. (©2020)
- Record Type:
- Book
- Title:
- Probabilistic mapping of spatial motion patterns for mobile robots. (©2020)
- Main Title:
- Probabilistic mapping of spatial motion patterns for mobile robots
- Further Information:
- Note: Tomasz Piotr Kucner, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, Chittaranjan Srinivas Swaminathan.
- Other Names:
- Kucner, Tomasz Piotr
Lilienthal, Achim J
Magnusson, Martin
Palmieri, Luigi
Srinivas Swaminathan, Chittaranjan - Contents:
- Intro -- Foreword -- Preface -- Objectives of This Book -- Content of This Book -- How to Read This Book? -- Acknowledgements -- Contents -- About the Authors -- Abbreviations -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Living in a Dynamic World -- 1.2 Robots in a Dynamic World -- 1.2.1 Static World Assumption -- 1.2.2 Information About Dynamics for Motion Planning -- 1.3 Examples of Application Scenarios -- 1.3.1 Scenario Descriptions -- 1.3.2 Application of Maps of Dynamics in the Scenarios -- 1.4 Challenges When Mapping Dynamics -- 1.4.1 Data Stochasticity 1.4.2 Partial Observability -- 1.4.3 Representation Design -- References -- 2 Maps of Dynamics -- 2.1 What Is a Map of Dynamics -- 2.1.1 Dynamics Perception -- 2.1.2 Dynamics Categorisation -- 2.1.3 Types of Maps of Dynamics -- 2.2 Overview of Maps of Dynamics -- 2.2.1 Mapping of Spatial Configuration Changes -- 2.2.2 Flow Modelling with Velocity Mapping -- 2.2.3 Trajectory Mapping -- 2.3 Motion Planning with Maps of Dynamics -- References -- 3 Modelling Motion Patterns with Conditional Transition Map -- 3.1 Introduction -- 3.2 Dynamics Extraction for T-CT-Map and CT-Map -- 3.2.1 Assumptions 3.2.2 Data Pre-processing -- 3.2.3 Occupancy Transition Detection -- 3.2.4 Spatial Transitions of Occupancy -- 3.3 Representations of Dynamics -- 3.3.1 Conditional Models -- 3.3.2 Parameter Learning -- 3.3.3 Conditional Probability Propagation Tree -- 3.4 Mapping Results -- 3.4.1 Toy Example -- 3.4.2 Mapping withIntro -- Foreword -- Preface -- Objectives of This Book -- Content of This Book -- How to Read This Book? -- Acknowledgements -- Contents -- About the Authors -- Abbreviations -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Living in a Dynamic World -- 1.2 Robots in a Dynamic World -- 1.2.1 Static World Assumption -- 1.2.2 Information About Dynamics for Motion Planning -- 1.3 Examples of Application Scenarios -- 1.3.1 Scenario Descriptions -- 1.3.2 Application of Maps of Dynamics in the Scenarios -- 1.4 Challenges When Mapping Dynamics -- 1.4.1 Data Stochasticity 1.4.2 Partial Observability -- 1.4.3 Representation Design -- References -- 2 Maps of Dynamics -- 2.1 What Is a Map of Dynamics -- 2.1.1 Dynamics Perception -- 2.1.2 Dynamics Categorisation -- 2.1.3 Types of Maps of Dynamics -- 2.2 Overview of Maps of Dynamics -- 2.2.1 Mapping of Spatial Configuration Changes -- 2.2.2 Flow Modelling with Velocity Mapping -- 2.2.3 Trajectory Mapping -- 2.3 Motion Planning with Maps of Dynamics -- References -- 3 Modelling Motion Patterns with Conditional Transition Map -- 3.1 Introduction -- 3.2 Dynamics Extraction for T-CT-Map and CT-Map -- 3.2.1 Assumptions 3.2.2 Data Pre-processing -- 3.2.3 Occupancy Transition Detection -- 3.2.4 Spatial Transitions of Occupancy -- 3.3 Representations of Dynamics -- 3.3.1 Conditional Models -- 3.3.2 Parameter Learning -- 3.3.3 Conditional Probability Propagation Tree -- 3.4 Mapping Results -- 3.4.1 Toy Example -- 3.4.2 Mapping with CT-Map -- 3.4.3 Mapping with T-CT-Map -- 3.5 Conclusions -- 3.5.1 Contributions -- 3.5.2 Limitations and Future Work -- References -- 4 Modelling Motion Patterns with Circular-Linear Flow Field Maps -- 4.1 Introduction -- 4.2 Representation -- 4.2.1 Velocity 4.2.2 Semi-Wrapped Normal Distribution -- 4.2.3 Semi-Wrapped Gaussian Mixture Model -- 4.2.4 Motion Ratio and Observation Ratio -- 4.3 Map Building -- 4.3.1 Data Discretisation -- 4.3.2 Mathematical Operations in Circular-Linear Space -- 4.3.3 Clustering -- 4.3.4 Fitting with the Expectation Maximisation Algorithm -- 4.3.5 Ridgeline Analysis -- 4.4 Map Densification -- 4.4.1 Monte Carlo Imputation -- 4.4.2 Nadaraya Watson Imputation -- 4.4.3 Trust Estimation -- 4.5 Evaluation Methodology -- 4.5.1 Bayesian Information Criterion -- 4.5.2 Divergence Estimator -- 4.5.3 k-fold Cross Validation 4.5.4 Stability Map -- 4.6 Evaluation -- 4.6.1 CLiFF-Map Toy Examples -- 4.6.2 Evaluation of Mapping -- 4.6.3 Evaluation of Densification -- 4.6.4 Guidelines for CLiFF-Map Building -- 4.7 Conclusions -- 4.7.1 Summary -- 4.7.2 Limitations -- 4.7.3 Future Work -- References -- 5 Motion Planning Using MoDs -- 5.1 Introduction -- 5.2 Sampling Based Motion Planning Using RRT* -- 5.2.1 RRT and RRT* -- 5.2.2 Steer Functions -- 5.3 Cost Functions for Motion Planning over MoDs -- 5.3.1 Down-The-CLiFF Cost (DTC) -- 5.3.2 Upstream Cost -- 5.3.3 A Note on the Cost Functions … (more)
- Publisher Details:
- Cham : Springer
- Publication Date:
- 2020
- Copyright Date:
- 2020
- Extent:
- 1 online resource (171 p.)
- Subjects:
- 519.2
Probabilities
Mobile robots
Mobile robots
Probabilities
Electronic books - Languages:
- English
- ISBNs:
- 9783030418083
3030418081 - Notes:
- Note: Includes bibliographical references.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.507464
- Ingest File:
- 03_083.xml