基于运动学约束的移动机器人组合导航算法  

Mobile Robot Integrated Navigation Algorithm Based on Enhanced Kinematic Constraint

作　　者：高春雷[1] 赵宾[1,2] GAO Chun-lei;ZHAO Bin(Jin Cheng College,Nanjing University of Aeronautics and Astronautics,Nanjing 211156,China;College of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学金城学院,南京211156 [2]南京航空航天大学自动化学院,南京211106

出　　处：《组合机床与自动化加工技术》2020年第9期6-10,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：江苏省高等学校自然科学研究项目资助(19KJD590001,18KJB590003)。

摘　　要：为提高GNSS受扰情况下移动机器人的组合导航性能,研究一种基于增强型运动学约束辅助的组合导航方法。分析移动机器人正常行驶过程中加速度特点构建组合导航系统状态模型,并在卫星/惯导位置、速度组合观测模型基础上,根据移动机器人侧向和垂向速度为零的约束条件构建虚拟观测模型,采用卡尔曼滤波进行组合导航算法设计。仿真验证结果表明,该方法可在不进行系统硬件更新的前提下,进一步提高GNSS受扰情况下移动机器人的组合导航性能,对工程应用具有重要的参考价值。In order to improve the performance of the mobile robot integrated navigation in the case of GNSS disturbance, an integrated navigation method based on enhanced kinematics constraint assistance was studied. The acceleration characteristics of the mobile robot during normal driving were analyzed to construct the state model of the integrated navigation system. Based on the GNSS/SINS position and velocity integrated observation model, the virtual observation model was constructed by using the constraints of zero side velocity and vertical velocity, and the kalman filter was used to design the integrated navigation algorithm. Simulation results show that, this method can further improve the mobile robot integrated navigation performance under the condition of GNSS being disturbed without increasing the sensor, and has important reference value for engineering application.

关 键 词：移动机器人 组合导航 运动学约束 GNSS受扰 卡尔曼滤波 

分 类 号：TH165[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]