基于SINS/GPS/DVL组合导航定位半实物仿真系统研究

为了满足水下航行远航程和长时间的要求,远航程自主水下航行器(AUV)采用以SINS导航为主、DVL导航为辅、卫星导航定期修正的方式来提高导航的精度和可靠性;文中研究了一种采用惯性测量器件(IMU)、GPS卫星定位导航模拟器、GPS接收机、多普勒测速仪仿真装置、ADI/RTS仿真工作站和水压模拟器构成的采用SINS/GPS/DVL组合导航方式的AUV导航半实物仿真系统,并进行了全航程仿真实验;仿真试验的结果表明,所设计的半实物仿真系统方案可行,可用于更高置信度的AUV组合导航仿真实验。     

SINS/DVL组合导航系统的标定

为提高智能水下机器人中的SINS/DVL组合导航系统定位精度,需要准确标定出捷联惯性导航系统(SINS)和多普勒计程仪(DVL)之间的安装误差角以及DVL的刻度系数。该方法只需AUV潜水一段时间后浮出水面2~3次,以接收的GPS定位信息作为参照,经过迭代计算,即可标定出DVL速度刻度系数和SINS与DVL之间的安装误差角。试验结果表明,用该方法能简单有效地标定出组合导航系统的各项误差参数,而且在多次标定修正后,定位精度优于7 m,具有较高的实用价值。     

惯性基高精度组合导航方法研究与仿真

研究了一种基于捷联惯性导航系统(SINS)的高精度组合导航方法;选取SINS的系统误差作为组合导航系统状态,利用天文导航系统(CNS)输出的姿态矩阵和SINS输出信息计算得到的等效姿态矩阵来构造量测,设计SINS/CNS组合导航算法;利用SINS与北斗卫星导航系统(RDSS)各自的位置输出构造量测,设计SINS/RDSS组合导航算法,从而,利用联邦卡尔曼滤波技术设计SINS/CNS/RDSS组合导航算法;仿真结果表明,惯性基SINS/CNS/RDSS组合导航方法具有较高的导航定位精度,工程应用前景良好。     

基于INS/GPS/COMPASS的AUV组合式导航定位算法

导航定位问题是自主式水下机器人研究(AUV)的主要内容之一,本文针对一种开架式AUV设计了一种采用间接反馈校正的捷联惯性导航与GPS、罗盘相组合的导航方案,其中采用卡尔曼滤波器接收两套导航子系统对同一导航参数输出值的差值,经过滤波计算估计出各误差量。仿真实验的结果表明,SINS/GPS/COMPASS组合导航对SINS误差随时间不断加大的现象起到了很好的抑制作用,能够满足AUV定位精度的要求。     

洋流影响下基于运动矢径的AUV协同定位方法

针对水下自主航行器（AUV）协同定位受水下未知定常洋流影响的问题,给出一种洋流影响下基于运动矢径的AUV协同定位方法.利用AUV的运动学方程和基于运动矢径的量测方程,建立AUV的导航模型;通过扩展的卡尔曼滤波,设计了协同定位滤波算法;利用该算法对洋流速度进行估计,以补偿AUV定位误差.仿真结果表明,该算法能有效估计未知定常洋流速度的大小,并对AUV定位误差进行实时补偿,显著提高了AUV的定位精度.     

RFID辅助捷联定位系统的地铁列车组合定位系统建模与仿真

为了提高地铁列车的导航定位精度,提出了一种基于射频识别(RFID)辅助捷联惯性定位系统(SINS)的列车组合定位系统,分析了捷联系统和RFID系统的误差源,并建立了数学模型,设计了地铁列车组合导航定位算法并进行了仿真实验。仿真结果表明,所提出的RFID辅助地铁列车定位系统精度和可靠性得到提高,克服了捷联惯性系统误差随时间增大和RFID定位信息不连续的缺陷。     

低成本SINS/GPS组合导航系统方案

低成本捷联惯性导航系统SINS、GPS硬件和相应的组合导航算法已经开始成熟,但仍然缺少简单可行的、完整的组合系统方案.针对低成本SINS\GPS组合导航设计了一套完整的方案.首先利用GPRS和TCP/IP通信链路实时传输GPS差分数据,提高GPS定位精度.用计算机串口接收SINS\GPS数据,并利用计算机时间使SINS和GPS数据同步.然后给出了SINS速度和位置更新的简化算法,由于低成本SINS无法确定航向角,所以使用SINS自带的姿态和航向参考系统输出的航姿信息.最后阐述了方案采用的组合导航数据融合卡尔曼滤波模型,并以RTK定位数据为参考真值进行了车载实验,实验表明组合系统更加稳健,定位精度明显提高.     

SINS/DVL水下组合导航技术发展综述

随着对海洋的探索和开发不断深入, 基于捷联惯性导航系统和多普勒计程仪相结合的水下组合导航技术, 近年来在水下无人航行器导航定位得到了广泛应用. 本文简要概述了捷联惯性导航系统/多普勒计程仪(SINS/DVL) 组合导航系统的基本架构, 列举了几种被广泛中应用于组合导航系统的信息融合技术. 通过对组合导航技术梳理分 析, 总结出近期研究的3个热点问题, 包括初始对准技术、标定技术、鲁棒性技术, 以技术的更新和优化为依托, 详细 阐述了3项技术的发展历程. 在总结归纳现有技术和研究成果的基础上, 展望并分析SINS/DVL组合系统将来的研究 方向及其面临的挑战. 本文可为高精度水下导航技术研究提供有益参考.     

扩展卡尔曼滤波在组合导航中的应用

松组合导航中,采用组合导航系统(SINS)误差作为状态量,用SINS解算的位置和速度与全球定位系统(GPS)测量的位置速度之差作为量测信息.为了提高组合导航的精度在松组合导航中应用扩展卡尔曼(EKF)滤波算法,通过仿真对载体轨迹的速度、姿态、位置进行跟踪.仿真结果表明:在仿真进入8 min之后系统进入稳态,能准确跟踪载体.因此,采用基于EKF的非线性滤波能有效跟踪载体的位置、速度和姿态.     

基于移动长基线的多AUV 协同导航

基于扩展卡尔曼滤波（EKF）理论研究了多AUV 协同导航定位的移动长基线算法．移动长基线多AUV 协同导航结构中,主AUV 内部装备高精度导航设备,从AUV 内部装备低精度导航设备,外部均装备水声装置测量 相对位置关系,利用移动长基线算法融合内部和外部传感器信息,实时获取从AUV 的位置信息．建立了协同导航 系统数学模型,设计了EKF 协同导航算法,在各种测试情况下通过仿真验证了所推导的分析结果,对EKF 和几何 解方程算法的导航效果进行了比较．研究结果表明,以主AUV 作为移动的长基线节点时,通过EKF 算法可以显著 提高群体的导航定位精度．     

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.     

海流环境下多AUV多目标生物启发任务分配与路径规划算法

针对多障碍物海流环境下多自治水下机器人(AUV)目标任务分配与路径规划问题, 本文在栅格地图构建的 基础上给出了一种基于生物启发神经网络(BINN)模型的新型自主任务分配与路径规划算法, 并考虑海流对路径规 划的影响. 首先建立BINN模型, 利用此模型表示AUV的工作环境, 神经网络中的每一个神经元与栅格地图中的位 置单元一一对应; 接着, 比较每个目标物在BINN地图中所有AUV的活性值, 并选取活性值最大的AUV作为它的获 胜AUV, 实现多AUV任务分配; 最后, 考虑常值海流影响, 根据矢量合成算法确定AUV实际的航行方向, 实现AUV路 径规划与安全避障. 海流环境下仿真实验结果表明了生物启发模型在多AUV水下任务分配与路径规划中的有效性.     

MEMS SINS-GPS组合导航系统设计

为实现满足中低精度要求的低成本导航系统,选用MEMS惯性传感器研制了捷联式惯性导航系统(SINS);针对MEMS惯性传感器噪声较大和惯性导航系统误差随时间迅速累积的问题,利用小波对MEMS陀螺信号进行了降噪处理,并采用SINS-GPS卡尔曼滤波组合导航系统以消除惯导系统的误差累积,输出较高精度的速度、位置信息.对SINS和组合导航系统进行了仿真实验,实验结果表明所建系统的长时间导航性能有一定改善.     

Mid-water current aided localization for autonomous underwater vehicles

Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.     

A high-resolution AUV navigation framework with integrated communication and tracking for under-ice deployments

We developed an environmentally adaptive under-ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on-board hydrodynamic model-aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice-tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self-calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one-way-travel-times (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model-assisted real-time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom-track aided INS during pre-ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN-ICNN framework was tested during ICEX20, which provided a nondiverging high-resolution navigation solution—with the majority of error below 15 m—that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid-water column.