A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots
A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots
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DOI：
10.1016/j.eswa.2010.02.059
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
A. Chatterjee;F. Matsuno
中科院分区：
文献类型：
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作者：
A. Chatterjee;F. Matsuno
本论文展示了如何最近提出的修改粒子群优化（PSO）算法，称为鹅PSO算法，可以用来调整模糊监督的自适应扩展卡尔曼滤波器（EKF）为基础的方法来解决同时定位和地图（SLAM）问题的移动的机器人或车辆。用于SLAM问题的这种类型的基于模糊的自适应EKF方法最近已被证明是在过程和/或传感器/测量不确定性统计的正确先验知识（即，分别为Q和/或R）不可用的那些情况下提高性能的有效方法。在这项工作中，新提出的系统被证明提供更好的估计和地图建设性能相比，模糊监督的自适应EKF算法，其中的自由参数的模糊系统使用基本的PSO算法进行调整。所提出的方法的效用是恰当地证明了采用它的几个基准环境的情况下，不同数量的航点和地标，鹅PSO算法可以调整模糊监督比基本的PSO算法。
The present paper shows how a recently proposed modified Particle Swarm Optimization (PSO) algorithm, called Geese PSO algorithm, can be utilized to tune a fuzzy supervisor for an adaptive Extended Kalman filter (EKF) based approach to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. This type of fuzzy based adaptive EKF approach for SLAM problems has recently been shown to be an effective approach to improve performance in those situations where correct a priori knowledge of process and/or sensor/measurement uncertainty statistics i.e. Q and/or R respectively, is not available. The newly proposed system in this work is demonstrated to provide better estimation and map-building performance in comparison with those fuzzy supervisors for the adaptive EKF algorithm, where the free parameters of the fuzzy systems are tuned using basic PSO based algorithm. The utility of the proposed approach is aptly demonstrated by employing it for several benchmark environment situations with various numbers of waypoints and landmarks, where the Geese PSO algorithm could tune the fuzzy supervisor better than the basic PSO based algorithm.