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References (25)
-
K. Burrage, Ian Lenane, G. Lythe (2007)
Numerical Methods for Second-Order Stochastic Differential EquationsSIAM J. Sci. Comput., 29
-
A. Gasparri, S. Panzieri, F. Pascucci, G. Ulivi (2007)
A Spatially Structured Genetic Algorithm over Complex Networks for Mobile Robot LocalisationProceedings 2007 IEEE International Conference on Robotics and Automation
-
K. Burrage, P. M. Burrage (2003)
Numerical methods for stochastic differential equations with applications -
A. Doucet, Nando Freitas, N. Gordon (2001)
Sequential Monte Carlo Methods in Practice -
V. Volterra (1931)
Animal ecology -
P. Verhulst
Recherches mathématiques sur la loi d’accroissement de la populationNouveaux mémoires de l'Académie royale des sciences et belles-lettres de Bruxelles
-
V. Volterra (1928)
Variations and Fluctuations of the Number of Individuals in Animal Species living togetherIces Journal of Marine Science, 3
-
M. Arulampalam, S. Maskell, N. Gordon, T. Clapp (2002)
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Trans. Signal Process., 50
-
Zhongqiang Zhang, G. Karniadakis (2017)
Numerical methods for stochastic differential equations -
D. Austin, P. Jensfelt (2000)
Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localizationProceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2
-
Wolfram Burgard, Andrcas Derr, D. Fox, A. Cremers (1998)
Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approachProceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190), 2
-
This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation -
D. Fox, W. Burgard, S. Thrun (1999)
Markov localization for mobile robots in dynamic environmentsJournal of Artificial Intelligence Research, 11
-
T. Malthus (2001)
Essay on the Principle of Population -
D. Fox, Wolfram Burgard, S. Thrun (2002)
Expert-Guided Subgroup Discovery: Methodology and ApplicationJ. Artif. Intell. Res., 17
-
Hans Moravec, A. Elfes (1985)
High resolution maps from wide angle sonarProceedings. 1985 IEEE International Conference on Robotics and Automation, 2
-
Wolfram Burgard, D. Fox, D. Hennig, Timo Schmidt (1996)
Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids -
Pradipta Sarkar (2003)
Sequential Monte Carlo Methods in PracticeTechnometrics, 45
-
F. Wilcoxon (1945)
Individual Comparisons by Ranking MethodsBiometrics, 1
-
Gerhard Venter, J. Sobieszczanski-Sobieski (2018)
Particle Swarm OptimizationAdvances in Metaheuristic Algorithms for Optimal Design of Structures
-
S. Schnell, T. Turner (2004)
Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws.Progress in biophysics and molecular biology, 85 2-3
-
G. Kitagawa (1996)
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space ModelsJournal of Computational and Graphical Statistics, 5
-
(1997)
Monte carlo methods for bayesian estimation of hidden markov models -
(2002)
A New Approach to Linear Filtering and Prediction Problems -
P. Jensfelt, S. Kristensen (2001)
Active global localization for a mobile robot using multiple hypothesis trackingIEEE Trans. Robotics Autom., 17
- Publisher
- Springer Journals
- Copyright
- Copyright © 2007 by Springer Science+Business Media, LLC
- Subject
- Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
- ISSN
- 0929-5593
- eISSN
- 1573-7527
- DOI
- 10.1007/s10514-007-9076-1
- Publisher site
- See Article on Publisher Site
Abstract
Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons.
Journal
Autonomous Robots – Springer Journals
Published: Jan 5, 2008