Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization
Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization
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DOI：
10.1016/j.robot.2012.09.018
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发表时间：
2012-12-01
影响因子：
4.3
通讯作者：
Myung, Hyun
中科院分区：
文献类型：
--
作者：
Lee, Hyoungki;Jung, Jongdae;Myung, Hyun
提高航位推算定位精度是机器人定位系统的关键技术之一，也是目前研究的热点。然而，现有的解决方案不能提供准确的定位时，机器人遭受不断变化的动态，如车轮打滑。在本文中，我们提出了一个模糊逻辑辅助交互多模型（FLAIMM）框架来检测和补偿车轮打滑。首先，设计了两种不同类型的扩展卡尔曼滤波器（EKF）来同时考虑移动的机器人的无滑移和滑移动力学。利用自适应神经模糊推理系统（ANFIS）建立了滑移率估计的模糊推理系统（FIS）模型。训练后的模型与FLAIMM框架中的两个EKF一起沿着使用。使用真实的数据集与机器人驾驶在室内环境中获得的方法进行评估。实验结果表明，与传统的多模型方法相比，该方法提高了位置精度，并具有更好的滑动检测和补偿效果。(C)2012爱思唯尔有限公司版权所有。
Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach. (C) 2012 Elsevier B.V. All rights reserved.