Numerical computation and analysis of high-speed autonomous underwater vehicle (AUV) moving in head sea based on dynamic mesh

Autonomous underwater vehicles （AUVs） navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions. The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied. The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory, which was solved to get the heave and pitch of the AUV by Gaussian elimination method. Based on this, computational fluid dynamics （CFD） method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes （RANS） equations, shear-stress transport （SST） k-w model and volume of fluid （VOF） model. The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition. The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function （UDF） based on dynamic mesh. The hydrodynamic parameters of the AUV such as drag, lift, pitch torque, velocity, pressure and wave profile were got, which reflect well the real ambient flow field of the AUV navigating in head sea at high speed. The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle. The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively, which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environnaent.

Sonar Image Processing System for an Autonomous Underwater Vehicle(AUV)

Sonar image processing system is an important intelligent system of Autonomous Un-derwater Vehicle.Based on TMS320C30 high speed DSP,it is used to realize sonar imagecompression and underwater object detections including obstacle recognition in real time.Inthis paper,the software and hardware designs of this system are introduced and the experi-mental results are given.

面向海流扰动和通信时延的欠驱动AUV编队跟踪控制

由欠驱动自主水下航行器(Autonomous Underwater Vehicle,AUV)编队组成的水下探测阵列具有响应快、机动性好、智能化程度高的特点。针对无速度信息传输的多AUV在海流和时延复合扰动下的编队控制问题,提出一种虚拟轨迹、轨迹预测、自适应反演滑模控制技术相结合的编队控制方法。使用领航者的位置信息和期望队形得到跟随者的参考轨迹,并引入虚拟轨迹使其与跟随者参考轨迹有限时间重合,从而得到跟随者的期望位置和速度。在此基础上,使用最小二乘法拟合轨迹曲线进行时延补偿,并通过自适应反演滑模控制技术完成海流干扰下的编队轨迹跟踪。理论分析和仿真结果表明,新方法具有可行性和有效性。

深海AUV无动力垂直下潜运动特性研究

深海AUV(自主水下机器人)采用无动力下潜方式能有效节约能源,但是水平或小纵倾角姿态下潜用时较长,水平偏移大。为提高下潜速度,减小下潜偏移距离,对AUV无动力垂直下潜过程进行研究。使用四元数代替欧拉角得到AUV运动方程,并加入洋流和浮力数学模型,通过Simulink仿真模块建立AUV垂直下潜运动仿真方法,解决垂直下潜产生的姿态角解算奇异性问题。通过设置不同仿真工况,得到AUV垂直下潜运动状态量受负浮力、重浮心距离、舵角、初始纵倾角及环境扰动力的作用规律。结果表明,AUV采用垂直下潜方式,可实现较大的下潜速度,同时减小水平偏移距离;洋流扰动是垂直下潜运动产生水平偏移的主要原因;下潜深度6000 m时,浮力变化扰动导致末端下潜速度减小30%,下潜时间增加21%。

基于COMGRU的AUV航路轨迹预测方法

针对采用神经网络预测自主水下机器人航迹存在滞后性的问题,本文提出一种基于信息压缩的改进门控循环神经网络,用于水下自主机器人航路多步轨迹预测。该算法将水下自主机器人航行轨迹附近的障碍物位置信息、海流信息以及时空轨迹信息共同构成的地理位置信息进行数据压缩处理,作为本文预测网络的输入,以提高网络训练效率。实验验证该算法减少了水下自主机器人航迹多步预测的滞后性且具有较高的准确率。

基于改进T分布烟花-粒子群算法的AUV全局路径规划

针对传统粒子群算法在处理自主水下机器人(Autonomous Underwater Vehicle,AUV)全局路径规划时面临的寻优时间长、能耗高的问题,本文提出一种改进的T分布烟花-粒子群算法(T-distribution Fireworks-Particle Swarm Optimization Algorithm,TFWA-PSO),该算法融合了烟花算法的高效全局搜索能力和粒子群算法的快速局部寻优特性.在变异阶段,提出自适应T分布变异来扩大搜索范围,并在理论上证明了该变异方式能够使个体在局部最优解附近增强搜索能力.在选择阶段提出了适应度选择策略,淘汰适应度差的个体,解决了传统烟花算法易丢失优秀个体的问题,并对改进的T分布烟花算法与传统烟花算法的收敛速度进行对比.将改进算法的爆炸操作、变异操作和选择策略融合到粒子群算法中,对粒子群算法的速度更新公式进行了改进,同时从理论上对所改进的算法进行了收敛性证明.仿真实验结果表明,TFWA-PSO能够有效规划出一条最短路径,同时与给定的智能优化算法相比,TFWA-PSO在寻找最优路径的时间上平均降低了24.72%,能耗平均降低了17.33%,路径长度平均降低了16.96%.

复杂环境下DWA与RRT算法融合的AUV局部路径规划

针对复杂水下环境下的自主水下航行器(autonomous underwater vehicle,AUV)局部路径规划问题,传统动态窗口法(dynamic window approach,DWA)存在复杂障碍物中陷入局部停滞,动态避障性能不佳等问题,本文提出了一种基于DWA与快速随机搜索树(rapid-exploration random tree,RRT)算法融合的路径规划算法。改进的DWA算法速度空间根据整个动态窗口的周期生成,重设了评价函数并结合AUV任务环境引入洋流能耗评价函数;改进的RRT算法在局部已知空间内规划导引点,帮助DWA脱离局部停滞状态并实现更安全的动态避障。将2种算法融合,实现了AUV在复杂水下环境中的局部路径规划。仿真表明,该融合算法能够降低AUV在洋流中的能耗代价,解决了DWA在复杂障碍物中陷入局部停滞的问题,能够安全有效地躲避动态避障物。

面向海底管道巡检的AUV三维自适应路径跟踪

针对面向海底管道巡检任务的欠驱动自主水下航行器(AUV)三维路径跟踪控制问题进行研究。首先,在固定坐标系和机体坐标系下建立AUV的运动学模型,为设计适宜运动控制的三维路径跟踪控制器,建立流体坐标系,并在Serret-Frenet坐标系下建立AUV的路径跟踪误差模型;其次,将可变前视距离引入传统视线(LOS)制导策略,提出一种自适应LOS制导策略以提高制导精度,并基于此设计面向海底管道巡检的AUV三维路径跟踪控制器,使得AUV可有效跟踪设定的参数化路径;最后,基于李雅普诺夫稳定性理论证明了闭环系统的稳定性。仿真结果验证了所提面向海底管道巡检的三维路径跟踪控制器的有效性。研究成果能够改善AUV的操纵性,可为面向海底管道巡检的无人水下航行器路径跟踪控制方法提供参考。

输入时延与通信时延下AUV集群的牵制控制

针对时延问题对集群稳定性的影响,对牵制控制策略进行研究,利用反馈线性化将水下无人潜航器(AUV)模型简化为二阶积分器模型,研究了具有时延的水下无人潜航器集群下的编队控制问题,并将一致性控制与牵制控制相结合。分析了输入时延和通信时延对水下无人潜航器集群系统的影响,应用Nyquist判据控制理论进行证明,得到了集群中被牵制水下无人潜航器和未被牵制水下无人潜航器组成的系统能渐进收敛达到一致性的充分必要条件。通过数值仿真验证了定理的正确性。

基于动态矩阵方法的欠驱动AUV运动控制研究

为解决欠驱动自主水下机器人(AUV)在航行过程中的控制问题,提高其抵抗外部干扰的能力,设计一种新型控制器,该控制器以模型预测控制策略中的动态矩阵控制(DMC)方法为主回路控制器,其目的是通过模型预测与反馈校正的特点使AUV获得良好的跟踪能力。此外,副回路控制器采用频率较高的增量式PID的控制方法,通过优化模型预测向量,设计模型调控参数,最终获得反应速度良好的内回路控制器,其作用是快速抑制突发性扰动。最后,将此控制器应用于AUV首向与深度的仿真实验中,通过仿真实验,验证了该控制器不仅能够满足AUV的控制需求,同时在面对外部时变干扰时具有较好的鲁棒性。

基于粒子群优化-人工势场的多AUV拦截技术研究

在复杂多变的水下环境中,针对港口等环境下的运动入侵目标的拦截问题,提出了一种新的多自主水下机器人(autonomous underwater vehicle,AUV)拦截算法——预测规划拦截法。首先,通过AUV和入侵目标的运动轨迹,依据拦截点思想快速简单地确定拦截位置,对三维环境下拦截点难以计算的问题采用粒子群优化算法选择最优拦截点,有效降低拦截距离。其次,利用人工势场法规划每个AUV在海流环境下的拦截路径。当目标物被环境中任意一个AUV拦截时,则认为拦截成功。仿真结果表明,在存在海流、障碍物的不同水下环境中,预测规划拦截法的拦截效率较传统的跟踪拦截法有较大的提升。

基于改进蚁群优化算法的AUV三维路径规划

针对蚁群算法在三维路径规划时收敛速度慢且难以收敛至最优的缺点,提出一种新的改进蚁群算法,并将其应用于自主式水下机器人(autonomous underwater vehicle,AUV)三维路径规划。与现有算法相比,改进算法优点主要体现在3个方面:首先,引进伪随机状态转移概率提升算法全局搜索能力;其次,将距离和轨迹限定因子引入启发式函数,距离因子保证搜索不断趋近目标点,在轨迹限定因子约束下,轨迹累计转角更小,以此提升收敛速度和精度;最后,通过扩大信息素增量差距并逐步提高信息素衰减系数,进一步提高路径规划效率。实验结果表明,改进蚁群算法能够获得累计转角更小路径,且路径长度更小,收敛速度更快。

基于L-SHADE算法的AUV载体磁干扰参数辨识的数值模拟

采用自主水下航行器(Autonomous Underwater Vehicle,AUV)磁测平台可开展海洋地磁场测量、水下磁性目标探测和识别等工作,AUV磁测平台具有广阔的应用前景,但目前AUV载体磁干扰补偿技术研究尚不成熟,制约着水下航行器测磁精度。基于磁测平台抗磁干扰基本原理,提出一种基于线性种群规模缩减和成功历史的参数自适应差分进化(Success History-based Adaptive Differential Evolution with Linear Population Size Reduction,L-SHADE)算法的AUV载体磁干扰参数辨识的数值模拟方法。用磁偶极子和旋转椭球壳混合模型来等效模拟AUV载体磁干扰,通过模拟航行获得多组磁测数据,据此建立磁干扰参数辨识模型,并采用L-SHADE算法求解。通过数值模拟实验定量分析研究磁测平台测磁精度随磁传感器、平台姿态及航向等误差的传播规律。研究结果表明:当磁传感器测量精度为10 nT、姿态测量精度为0.01°、航向测量精度为0.1°时,测磁误差可小于100 nT。设计的AUV磁测平台抗干扰试验表明,地磁场总量最大相对误差为1.07%。

一种AUV模块化载体结构设计与实现

随着科技的不断发展,水下作业任务呈多样化、复杂化的趋势,针对自主式水下无人航行器功能多样性的问题,采用模块化的设计思想,提出一种载体结构设计方法。以某300 m级探测型AUV为例,对本文提出的载体结构进行验证。水池试验结果表明,本文提出的模块化载体结构运行情况良好,保证了AUV各项功能的实现,可为水下无人航行器的工程设计提供参考。

Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks(RNN) is proposed. Firstly, considering the inaccu?rate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty(SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering?reduction method is proposed based on sigmoid function. In chattering?reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapu?nov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can e ectively achieve high?precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and e ectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool?experi?ments of AUV.

一种绳驱动AUV布放回收装置的结构设计与动力学分析

为了提高自主式水下航行器(AUV)布放回收的安全性和效率,以船舶起重机为基础,设计一种不仅能通过绳索自主调整吊笼的姿态,而且具有良好减摇效果的AUV水面布放回收装置。分析该装置的结构特性和机械理论模型,采用坐标转换法对吊笼的运动学位置、速度和加速度进行分析,并通过MATLAB软件对其进行仿真验证。在运动学模型的基础上对装置进行动力学分析,并利用ADAMS搭建绳索系统的虚拟样机并进行无激励和存在激励仿真验证。研究结果表明:在受船舶激励的情况下,横摇的减摇效果为78%,纵摇的减摇效果为75%;在给定轨迹下,4根绳索的变化比较稳定,能通过绳索调整吊笼的姿态,进而安全高效地完成AUV布放回收工作。

Bio-inspired geomagnetic navigation method for autonomous underwater vehicle

This paper presents a bio-inspired geomagnetic navigation method for autonomous underwater vehicle(AUV) without using any a priori geomagnetic information. Firstly, the multi-objective search problem is raised. Secondly, the geomagnetic navigation model is established by constructing a cost function. Then, by taking into consideration the biological magneto-taxis movement behavior for the geomagnetic environment stimulus, the multiobjective evolutionary search algorithm is derived to describe the search process. Finally, compared to the state-of-the-art, the proposed method presents better robustness. The simulation results demonstrate the reliability and feasibility of the proposed method.

Consensus of multiple autonomous underwater vehicles with double independent Markovian switching topologies and timevarying delays

A new method in which the consensus algorithm is used to solve the coordinate control problems of leaderless multiple autonomous underwater vehicles（multi-AUVs） with double independent Markovian switching communication topologies and time-varying delays among the underwater sensors is investigated.This is accomplished by first dividing the communication topology into two different switching parts,i.e.,velocity and position,to reduce the data capacity per data package sent between the multi-AUVs in the ocean.Then,the state feedback linearization is used to simplify and rewrite the complex nonlinear and coupled mathematical model of the AUVs into a double-integrator dynamic model.Consequently,coordinate control of the multi-AUVs is regarded as an approximating consensus problem with various time-varying delays and velocity and position topologies.Considering these factors,sufficient conditions of consensus control are proposed and analyzed and the stability of the multi-AUVs is proven by Lyapunov-Krasovskii theorem.Finally,simulation results that validate the theoretical results are presented.

Reinforcement Learning Based Obstacle Avoidance for Autonomous Underwater Vehicle

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV)in an unknown underwater environment during exploration process.Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties,disturbance,or plant model mismatch.On the other hand,model-free reinforcement learning(RL)algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach.Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment.A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore,function approximation is utilized using neural network(NN)to overcome the continuous states and large statespace problems which arise in RL-based controller design.The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance.Also,the same algorithm is utilized to deal with multiple obstacle avoidance problems.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.