Robots from nowhere

Hatice Köse; H. Levent Akin

doi:10.1007/978-3-540-32256-6_56

Robots from nowhere

Hatice Köse^*, H. Levent Akin

^*Corresponding author for this work

Bogazici University

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

In this study, a new method called Reverse Monte Carlo Localization (R-MCL) for global localization of autonomous mobile agents in the robotic soccer domain is proposed to overcome the uncertainty in the sensors, environment and the motion model. This is a hybrid method based on both Markov Localization(ML) and Monte Carlo Localization(MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. The method is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.

Original language	English
Pages (from-to)	594-601
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3276
DOIs	https://doi.org/10.1007/978-3-540-32256-6_56
Publication status	Published - 2005
Externally published	Yes

Keywords

Global localization
MCL
ML
Robot soccer

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10.1007/978-3-540-32256-6_56

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title = "Robots from nowhere",

abstract = "In this study, a new method called Reverse Monte Carlo Localization (R-MCL) for global localization of autonomous mobile agents in the robotic soccer domain is proposed to overcome the uncertainty in the sensors, environment and the motion model. This is a hybrid method based on both Markov Localization(ML) and Monte Carlo Localization(MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. The method is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.",

keywords = "Global localization, MCL, ML, Robot soccer",

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doi = "10.1007/978-3-540-32256-6_56",

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AU - Köse, Hatice

AU - Akin, H. Levent

PY - 2005

Y1 - 2005

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AB - In this study, a new method called Reverse Monte Carlo Localization (R-MCL) for global localization of autonomous mobile agents in the robotic soccer domain is proposed to overcome the uncertainty in the sensors, environment and the motion model. This is a hybrid method based on both Markov Localization(ML) and Monte Carlo Localization(MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. The method is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.

KW - Global localization

KW - MCL

KW - ML

KW - Robot soccer

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U2 - 10.1007/978-3-540-32256-6_56

DO - 10.1007/978-3-540-32256-6_56

M3 - Conference article

AN - SCOPUS:26444609609

SN - 0302-9743

VL - 3276

SP - 594

EP - 601

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Robots from nowhere

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Keywords

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