Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation
Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation
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基于方差分量估计的室内环境下多移动机器人系统协同定位算法
DOI：
10.3390/sym9060094
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发表时间：
2017-06
期刊：
影响因子：
2.7
通讯作者：
Li Yibing
中科院分区：
文献类型：
--
作者：
Sun Qian;Diao Ming;Zhang Ya;Li Yibing
多移动的机器人（MMR）协作系统由于其高效、容错性好等优点，正成为各个领域的研究热点。然而，不确定性和非线性问题严重限制了MMR系统的协作定位精度。针对上述问题，本文提出了一种基于立方卡尔曼滤波（CKF）和自适应方差分量估计（VCE）的MMR系统协同定位算法。在该算法中，一个名为CKF的非线性滤波器被用来提高合作定位精度和减少计算量。另一方面，自适应VCE方法被引入到消除未知系统噪声的影响。利用真实的实验数据，将该算法与基于普通CKF的协同定位算法进行了性能比较。实验结果表明，该算法在定位精度和一致性方面均优于CKF协同定位算法。
The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.
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