摘要
移动机器人的导航及定位是机器人自主导航的关键技术之一。为提高移动机器人的导航及定位能力,提出以多种导航定位传感器组合为融合单元,设计扩展卡尔曼滤波算法,将陀螺仪、里程计和电子罗盘采集的数据进行融合。设计模糊神经网络对所融合的数据进行训练处理,提高数据处理的精度和效率,实现对移动机器人精确的控制。并进行了仿真分析,结果证明:所提出的多传感器信息融合算法既可使移动机器人在复杂环境中自主定位,又实现有效避障,有实际参考价值。
The navigation and positioning of mobile robots is one of the key technologies for autonomous robot navigation.In order to improve the navigation and positioning capabilities of mobile robots,a combination of multiple navigation and positioning sensors was proposed as a fusion unit,and an extended Kalman filter algorithm was designed to fuse the data collected by the gyroscope,odometer and electronic compass.A fuzzy neural network was designed to train and process the fused data,the accuracy and efficiency of data processing was improved,and precise control of the mobile robot was achieved.Finally,a simulation analysis was carried out.The results prove that using the proposed multi-sensor information fusion algorithm,the mobile robot can not only enable to locate autonomously in a complex environment but also achieve effective obstacle avoidance,which has practical reference value.
作者
邵明志
何涛
朱永平
陈文重
SHAO Mingzhi;HE Tao;ZHU Yongping;CHEN Wenchong(School of Mechanical Engineering,Hubei University of Technology,Wuhan Hubei 430068,China;Hubei Key Lab of Modern Manufacture Quality Engineering,Wuhan Hubei 430068,China)
出处
《机床与液压》
北大核心
2023年第5期8-13,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金面上项目(51275158)。
关键词
移动机器人
多传感器信息融合
扩展卡尔曼滤波
模糊神经网络算法
避障
Mobile robot
Multi-sensor information fusion
Extended Kalman filter
Fuzzy neural network algorithm
Obstacle avoidance
