A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

In this article a new Data‐Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor‐limited AUVs, where typical scenarios often include non‐negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data‐Driven approach.     

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.     

一种水下机器人运动的过程神经元控制

过程神经网络是传统神经网络的拓展, 增加了一个对于时间的聚合算子, 从而更好地模拟了生物神经元的信息处理机制. 这是由于水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量. 结合S函数和预先规划思想, 建立水下机器人过程神经元的运动控制模型. 仿真试验证明,该新型控制模型, 对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等优点.     

一种基于混沌过程神经元水下机器人运动控制方法

由于系统的强非线性以及不确定性,同时考虑到港湾环境下水声信号的噪声大,水下机器人进行精确作业时的运动控制一直是其实用化过程中困挠人们的问题。过程神经网络是传统神经网络的拓展,它增加了一个对于时间的聚合算子,使网络同时具有时空二维信息处理能力,从而更好地模拟了生物神经元的信息处理机制。水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量。在基本神经元模型上,结合S函数和预先规划思想,建立水下机器人过程神经元运动控制模型,参数学习过程中,将遍历性的渐变混沌噪声引入其中,增强控制器全局优化能力。仿真试验表明,该新型控制模型,对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等各种优点。     

低功耗水下自主导航系统设计

以微处理器586 -engine为控制核心,采用模型辅助的自主导航算法设计了一种水下自主导航系统.该系统能够实时采集多个外部传感器数据并对数据进行实时解算,同时满足低成本、低功耗和长航时的要求.搭建了整个系统硬件平台,通过陆上跑车实验对整个实时导航系统进行了满足一定精度的简单实验验证,验证了系统的实时性和可行性.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

基于海流观测器对欠驱动水下机器人进行三维路径跟随

对模型参数未知的欠驱动水下机器人,本文设计了海流中三维路径跟随控制器.该方法在无旋流的前提下,利用海流的运动方程,结合三维视线导航法和反步法,设计了控制器.通过死区方法估计模型参数,克服了参数漂移问题;针对海流未知情况,设计了海流观测器.最后通过Lyapunov稳定性理论证明了跟踪误差是收敛的,仿真实验验证了该控制方法的有效性.     

Underwater guidance of distributed autonomous underwater vehicles using one leader

Consider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles.     

水下机器人协同控制的TO模型区域划分定位

针对稀疏型水声传感器网络定位算法面临的定位覆盖率低和误差高的问题, 本文提出一种水下机器人协 同控制的截角八面体(TO)模型区域划分定位算法. 首先搭建定位系统模型, 提出TO模型满足三维目标区域划分原 则, 并证明其体积比相对最优; 然后设计TO模型最优区域划分方式, 提出最小值判定法进一步整合目标节点, 自主 水下机器人(AUVs)协同控制筛选包含目标节点的子区域; 通过分析通信半径和虚拟锚节点数量对实验结果的影响, 设置最优定位参数, 降低能耗和定位误差, 最后利用最小二乘法完成定位. 本文分别对定位覆盖率、子区域AUV路 径长度和定位精度进行了仿真实验, 结果表明, 相比于其他区域划分方案, 所提算法误差较小、定位覆盖率高且鲁 棒性强.