基于CKF的分布式多移动机器人协作定位  

Decentralized Multi-Mobile Robots Cooperative Localization Based on CKF

作　　者：王雪冬 芦鑫元 黄颖涛 吕鹏 WANG Xuedong;LU Xinyuan;HUANG Yingtao;LV Peng

机构地区：[1]西安导航技术研究所,西安710068 [2]陕西省组合与智能导航重点实验室,西安710068 [3]中国人民解放军93216部队,北京100085

出　　处：《现代导航》2024年第6期391-397,共7页Modern Navigation

摘　　要：针对GPS拒止环境的多移动机器人定位问题,提出了一种基于容积卡尔曼滤波(CKF)的分布式协作定位方法(DCL-CKF)。在该方法中每个移动机器人无需存储测量值,只需保持对自身的最新估计,且只有当两个移动机器人获得彼此的相对测量值时,才发生信息交换,节省了存储成本并提高了通讯效率。此外,通过引入CKF算法降低了非线性函数线性化的误差,提升了定位精度。蒙特卡罗仿真表明,DCL-CKF能够获得移动机器人状态一致估计,在定位精度上相较于分布式扩展卡尔曼滤波(EKF)定位算法平均提升了21.68%。实验结果表明,DCL-CKF能够获得移动机器人状态一致估计,在定位精度上相较于分布式EKF定位算法提升了32.25%。A decentralized algorithm utilizing the Cubature Kalman Filter(CKF)is proposed to address the multi-mobile robot localization problem in environments where GPS is unavailable,referred to as Decentralized Cooperative Localization Algorithm Based on CKF(DCL-CKF).In DCL-CKF,each mobile robot does not need to store the measurements but only maintains the latest estimate of itself.Information exchange only takes place between two robots when they obtain relative measurements of each other,saving storage costs and improving communication efficiency.Furthermore,by selecting a set of cubature points to approximately calculate the mean and covariance of the estimated state of the mobile robot,the error in linearizing the nonlinear observation function is reduced,thereby improving the localization accuracy.Monte Carlo simulations and a group of real-world experiments were conducted to verify the performance of the proposed DCL-CKF approach.The results show that the DCL-CKF can obtain consistent localization state estimation of mobile robots,and the average localization accuracy is improved by 21.68%and 32.25%compared with the decentralized Extended Kalman Filter(EKF)localization algorithm,respectively.

关 键 词：多移动机器人系统 协作定位 容积卡尔曼滤波 多传感器融合 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]