Michael John Milford

Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models - Springer Tracts in Advanced Robotics Michael John Milford 2008 edition

Marc Notes: Includes bibliographical references and index. Review Quotes: From the reviews: This book is written for researchers, graduate students, and professionals in robotics, especially robot navigation and computational neuroscience. The hippocampus has been studied extensively in rodents as part of the brain system responsible for navigation and spatial memory . (IEEE Control Systems Magazine, Vol. 30, April, 2010)"Table of Contents: List of Figures -- List of Abbreviations -- 1. Introduction -- 1.1. Mobile Robots -- 1.2. Simultaneous Localisation and Mapping -- 1.3. Exploration, Goal Navigation and Adapting to Change -- 1.4. Practical Performance from a Biological Model -- 1.5. Book Outline -- 2. Mapping and Navigation -- 2.1. The Mapping and Navigation Problem -- 2.1.1. Localisation and Mapping -- 2.1.2. Slam: The Chicken and the Egg Problem -- 2.1.3. Dealing with Uncertainty -- 2.1.4. Exploring Unknown Environments -- 2.1.5. Navigating to Goals -- 2.1.6. Learning and Coping with Change -- 3. Robotic Mapping Methods -- 3.1. Probabilistic Mapping Algorithms -- 3.1.1. Kalman Filter Methods -- 3.1.2. Expectation Maximisation Methods -- 3.1.3. Particle Filter Methods -- 3.2. Topological Mapping Methods -- 3.3. Exploration, Navigation, and Dealing with Change -- 3.3.1. Exploration -- 3.3.2. Navigating to Goals -- 3.3.3. Dealing with Dynamic Environments -- 3.4. Discussion -- 4. Biological Navigation Systems -- 4.1. Rodents and the Cognitive Map -- 4.1.1. Head Direction and Place Cells -- 4.1.2. Exploration, Navigation, and Dealing with Change -- 4.2. Other Animals and Insects -- 4.2.1. Bees -- 4.2.2. Ants -- 4.2.3. Primates -- 4.2.4. Humans -- 4.3. Discussion -- 5. Emulating Nature: Models of Hippocampus -- 5.1. Head Direction and Place Cells - State of the Art -- 5.1.1. Attractor Networks -- 5.1.2. Path Integration -- 5.1.3. Head Direction Correction Using Allothetic Information -- 5.1.4. Place Cells - State of the Art -- 5.1.5. Place Cells Through Allothetic Cues -- 5.1.6. Place Cells Through Ideothetic Information -- 5.1.7. Navigation -- 5.2. Discussion -- 6. Robotic or Bio-inspired: A Comparison -- 6.1. Robustness Versus Accuracy -- 6.2. Map Friendliness Versus Map Usability -- 6.3. Sensory Differences -- 6.4. Capability in Real World Environments -- 6.5. One Solution -- 7. Pilot Study of a Hippocampal Model -- 7.1. Robot and Environment -- 7.2. Complete Model Structure -- 7.3. A Model of Spatial Orientation -- 7.3.1. Representing Orientation -- 7.3.2. Learning Allothetic Cues -- 7.3.3. Re-localisation Using Allothetic Cues -- 7.3.4. Internal Dynamics -- 7.3.5. Path Integration Using Ideothetic Information -- 7.4. Model Performance -- 7.4.1. Experiment 1: Path Integration Calibration -- 7.4.2. Experiment 2: Localisation and Mapping in 1D -- 7.5. A Model of Spatial Location -- 7.5.1. Representing Location -- 7.5.2. Learning Allothetic Cues -- 7.5.3. Re-Localisation Using Allothetic Cues -- 7.5.4. Internal Dynamics -- 7.5.5. Path Integration Using Ideothetic Information -- 7.6. Model Performance -- 7.6.1. Experiment 3: Localisation and Mapping in 2D -- 7.7. Discussion and Summary -- 7.7.1. Comparison to Biological Systems -- 7.7.2. Comparison to Other Models -- 7.7.3. Conclusion -- 8. RatSLAM: An Extended Hippocampal Model -- 8.1. A Model of Spatial Pose -- 8.1.1. Complete Model Structure -- 8.1.2. Biological Evidence for Pose Cells -- 8.1.3. Representing Pose -- 8.1.4. Internal Dynamics -- 8.1.5. Learning Visual Scenes -- 8.1.6. Re-localising Using Familiar Visual Scenes -- 8.1.7. Intuitive Path Integration -- 8.2. Generation of Local View -- 8.2.1. Sum of Absolute Differences Module -- 8.2.2. Image Histograms -- 8.3. Visualising SLAM in a Hippocampal Model -- 8.4. Slam in Indoor and Outdoor Environments -- 8.4.1. Experiment 4: Slam with Artificial Landmarks -- 8.4.2. Experiment 5: Slam in a Loop Environment -- 8.4.3. Experiment 6: Slam in an Office Building -- 8.4.4. Experiment 7: Slam in Outdoor Environments -- 8.4.5. Path Integration only Performance -- 8.4.6. Slam Results -- 8.5. Summary and Discussion -- 8.5.1. RatSlam Requirements -- 8.5.2. The Nature of RatSlam Representations -- 9. Goal Memory: A Pilot Study -- 9.1. Enabling Goal Recall Using RatSlam -- 9.2. Learning -- 9.3. Recall -- 9.3.1. Experiment 8: Small Environment Goal Recall -- 9.3.2. Goal Recall Results -- 9.3.3. Experiment 9: Large Environment Goal Recall -- 9.3.4. Goal Recall Results -- 9.4. Summary and Discussion -- 9.4.1. Creating Maps Suited to Goal Recall -- 10. Extending RatSlam: The Experience Mapping Algorithm -- 10.1. A Map Made of Experiences -- 10.2. Linking Experiences: Spatially, Temporally, Behaviourally -- 10.3. Map Correction -- 10.4. Map Adaptation and Long Term Maintenance -- 10.5. Indoor Experience Mapping Results -- 10.5.1. Experiment 10: Large Pose Cell Representation -- 10.5.2. Experiment 11: Small Pose Cell Representation -- 10.6. Experiment 12: Outdoor Experience Mapping -- 10.7. Summary and Discussion -- 11. Exploration, Goal Recall, and Adapting to Change -- 11.1. Exploring Efficiently -- 11.1.1. Experimental Evaluation -- 11.1.2. Discussion -- 11.2. Recalling Routes Using a Temporal Map -- 11.2.1. Temporal Map Creation -- 11.2.2. Route Planning -- 11.2.3. Behaviour Arbitration -- 11.2.4. Route Loss Recovery -- 11.3. Slam and Navigation in a Static Environment -- 11.3.1. Experiment 13: Goal Recall with Minor Pose Collisions -- 11.3.2. Experiment 14: Goal Recall with Major Pose Collisions -- 11.3.3. Discussion -- 11.4. Adapting to Environment Change -- 11.4.1. Experiment 15: Indoor Map Adaptation -- 11.4.2. Results -- 11.5. Discussion -- 11.5.1. Conclusion -- 12. Discussion -- 12.1. Book Summary -- 12.1.1. Mapping and Navigation -- 12.1.2. Pilot Study of a Hippocampal Model -- 12.1.3. RatSlam: An Extended Hippocampal Model -- 12.1.4. Goal Memory: A Pilot Study -- 12.1.5. Extending RatSlam: Experience Mapping -- 12.1.6. Exploring, Goal Recall, and Adapting to Change -- 12.2. Contributions -- 12.2.1. Comparative Review of Robotic and Biological Systems -- 12.2.2. Performance Evaluation of Hippocampal Models -- 12.2.3. Implementation of an Extended Hippocampal Model -- 12.2.4. An Experience Mapping Algorithm -- 12.2.5. An Integrated Approach to Mapping and Navigation -- 12.3. Future Mapping and Navigation Research -- 12.4. Grid Cells -- 12.5. Conclusion -- Appendix A. The Movement Behaviours -- Search of Local Space -- Pathway Identification -- Velocity Commands -- References -- List of Reproduced Figures -- Index. Review Quotes: From the reviews: This book is written for researchers, graduate students, and professionals in robotics, especially robot navigation and computational neuroscience. The hippocampus has been studied extensively in rodents as part of the brain system responsible for navigation and spatial memory . (IEEE Control Systems Magazine, Vol. 30, April, 2010) Publisher Marketing: At the dawn of the new millennium, robotics is undergoing a major transformation in scope and dimension. From a largely dominant industrial focus, robotics is rapidly expanding into the challenges of unstructured environments. Interacting with, assi- ing, serving, and exploring with humans, the emerging robots will increasingly touch people and their lives. The goal of the new series of Springer Tracts in Advanced Robotics (STAR) is to bring, in a timely fashion, the latest advances and developments in robotics on the basis of their significance and quality. It is our hope that the wider dissemination of research developments will stimulate more exchanges and collaborations among the research community and contribute to further advancement of this rapidly growing field. The monograph written by Michael Milford is yet another volume in the series devoted to one of the hottest research topics in the latest few years, namely Simul- neous Localization and Map Building (SLAM). The contents expand the author s doctoral dissertation and describe the development of a robot mapping and navigation system inspired by models of the neural mechanisms underlying spatial navigation in the rodent hippocampus. One unique merit of the book lies in its truly interdisciplinary flavour, addressing a link between biology and artificial robotic systems that has been open for many years. To the best of my knowledge, this is the most thorough attempt to marry biological navigation in rodents and similar, with robotic navigation and SLAM. A very fine addition to our STAR series!"