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Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
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Mobile Robot Global Localization using an Evolutionary MAP Filter
Journal of Global Optimization
Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing
Computer Communications
Fast Genetic Scan Matching Using Corresponding Point Measurements in Mobile Robotics
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Being autonomous is one of the most important goals in mobile robots. One of the fundamental works to achieve this goal is giving the ability to a robot for finding its own correct position and orientation. Different methods have been introduced to solve this problem. In this paper, a novel method based on the harmony search (HS) algorithm for robot localization through scan matching is proposed. Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance. Furthermore a new hybrid algorithm based on harmony search and differential evolution (DE) algorithms is proposed and evaluated on different benchmark functions. Finally the hybrid algorithm has been applied for mobile robot localization and it outperformed the HS-based approach.