Robot Movement Optimaization with Using Localization Algorithms

World Applied Sciences Journal 8 (4): 422-428, 2010 ISSN 1818-4952 © IDOSI Publications, 2010

Robot Movement Optimaization with Using Localization Algorithms
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Omid Panah, 2Amir Panh, 1Amin Panah and 1Abolfazl Akbari

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Department of Computer Engineering, Islamic Azad University Ayatollah Amoli Branch, Amol, Iran 2 Department of Computer Engineering, Islamic Azad University Qazvin, Qazvinl, Iran

Abstract: The majority of localization algorithms start at a known position and add internal movement data and external environment data to this position each cycle. If the robot isreplaced or the sensor data quality is too low, these algorithms are usually not able to recover to a useful position estimation Members of these so-called local approaches are the linear least squares estimator and the Kalman filter. Robots equipped with global localization algorithms like Markov localization and particle filter are able to localize themselves even under global uncertainty. This Article focuses on local and global localization, static environments andpassive approaches. Active approaches have to be discussed along with the decision making. To be able to cope with dynamic environments, map building is necessary. Both topics are not within the scope of this work. Key words: Kalman Filter % LLSQ % Markov Localization % Particle Filter INTRODUCTION Localization, Estimate the location of the robot within the environment based on observations. These observations typically consist of a mixture of odometric information about the robot’s movements and information obtained from the robot’s proximity sensors or cameras. In first problems are presented which may occur while performing a localization task. After introducing a taxonomy for localization algorithms, four different types of them linear least squares filter, Kalman filter, Markov localization and particle filter are discussed. They are ordered by their complexity and by historic development. First the simplest...