水下活动 别具一格

水下象棋赛 澳大利亚两位国际象棋大师曾举行了一次海底象棋赛。两人身负沉甸甸的氧气瓶,穿着厚墩墩的潜水服,在海底摊开电子棋盘,足足鏖战了2个小时。目前,这一活动已流行于欧美。     

某型自卸船水下噪声分析

文章介绍了水下噪声的分类以及水下噪声相关环保法规的情况。描述了某型自卸船水下噪声测试的过程以及测试结果,通过和英国劳氏船级社法规相关指标要求的对比,表明本型船水下噪声除了在13 Hz和65 Hz频率段附近高出UNW-L (T2)的要求外,其他频率段均满足指标要求,基于现有法规,本型船可被授予UWN-M的船级符号。     

同步钟式水下测距综述

水下运动体辐射噪声测试必须建立相应的海上测试手段。其诸多手段中，实现不影响水下目标辐射噪声特性研究的水下实时精密测距是极为重要的，本文就此给予了一般性的论述，文章结合我们多年的工作实践。重点对各种同步钟式水下测距技术进行了较详尽讨论。     

水下无损检测技术的研究现状和发展趋势

文章介绍了水下构件的损伤形式和水下无损检测的技术手段,根据实践经验指出了目前存在的问题,并分析了今后的发展方向.     

国外水下无人装备研究现状及发展趋势

复杂的水文和海洋环境决定了水下是难以掌控的作战空间,也是世界军事强国竞相争夺的重要战略领域.随着新型水下作战概念的深化和水下无人技术的发展,水下无人装备在军、民、科研等方面迅速发展.为继续保持水下优势,美海军积极推进水下无人航行器军用化、体系化发展,加速全球水下监听、监视系统构建、加强战略前沿水下预置装备部署.本文综述国外在UUV、水下预置装备、水下监听网络等水下无人装备方面的发展规划,分析国外研究进展并总结发展趋势,为我国开展水下无人装备研究提供借鉴.     

水下战场的变化

介绍了美国海军水下作战中心(NUWC)正在用Manta试验运载器(MTV)来论证及评估开发未来的水下无人运载器(UUV)。设想将整个UUV固定在潜艇的首部，可以放出执行侦察、通信甚至作战等诸多任务，又能回收固定在宿主船的外壳上。如水下无人通信运载器(UUCV)可用于水下声通信，水面无线电波联系等；其尺寸可以为53英寸／70磅至重型鱼雷。在文中一张构想图中，则在UUV上还有4个鱼雷发射管。图5。     

水下通信定位导航技术分析及一体化展望

水下通信定位导航技术在海洋勘测、资源开发、海洋环境保护以及海洋军事行动等各种作业任务中都发挥着重要作用。本文通过对近年来水下通信技术和水下定位导航技术的文献进行收集整理,从而对以上技术进行分析,并且着重分析水下定位导航技术中的几种不同的方向。最后对水下通信定位导航一体化设计中所需要解决的关键技术进行分析及展望,对形成水下通信定位导航一体化组网、进一步提高水下通信的距离与可靠性以及提升定位导航的精确度提供研究基础。     

水下机器人的新情况、发展特点及启示

文章介绍了国内外水下机器人的新情况,概括了水下机器人发展特点,以及从中得到的启示.     

外军水下侦察技术现状与发展趋势

潜艇、无人潜航器、蛙人等水下装备在现代战争中发挥着越来越重要的作用,甚至会直接影响战斗的胜负.水下侦察是海洋立体侦察的重要组成部分,介绍了外军主要水下侦察技术的现状,分析了各种技术的原理与发展趋势,最后对我军水下侦察技术的发展提出了展望.     

水下管汇阀门的选型和材料要求

考虑到水下管汇在深水开发中的重要性,对水下管汇阀门的选型和材料技术进行研究,结合规范要求和实际水下工程设计经验,提出水下管汇阀门的选型设计流程以及阀门材料的技术要求。     

基于差分进化算法的自主式水下航行器在时变水下环境中的快速路径规划方法（英文）

Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution(DE) algorithm is employed. The performance of the DE-based planner in generating time-efficient paths to direct the AUV from its initial conditions to the target of interest is investigated within a complexed 3D underwater environment incorporated with turbulent current vector fields, coastal area,islands, and static/dynamic obstacles. The results of simulations indicate the inherent efficiency of the DE-based path planner as it is capable of extracting feasible areas of a real map to determine the allowed spaces for the vehicle deployment while coping undesired current disturbances, exploiting desirable currents, and avoiding collision boundaries in directing the vehicle to its destination. The results are implementable for a realistic scenario and on-board real AUV as the DE planner satisfies all vehicular and environmental constraints while minimizing the travel time/distance, in a computationally efficient manner.     

基于改进A*GBNN算法的AUV路径规划研究

针对现有的离散生物启发神经网络(Glasius bioinspired neural networks, GBNN)算法在未知环境下，存在的路径规划时间长、易陷入局部最优等问题，提出一种结合A*与GBNN模型的改进算法。在GBNN活性值栅格网络中，算法将各栅格的活性值作为A*的代价函数进行运算并使用跳点搜索规则优化，实现未知环境下的实时路径规划。仿真实验结果表明，该算法有效改善了自主水下航行器在未知环境下的寻路效率，可以满足自主水下航行器实时路径规划需求。     

Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV

To research the effect of the selection method of multi-objects genetic algorithm problem on optimizing result, thismethod is analyzed theoretically and discussed by using an autonomous underwater vehicle(AUV) as an object. A changingweight vtlue method is put forward and a selection formula is modified. Some experiments were implemented on an AUV.TwinBurger. The results shows that this method is effective and feasible.     

一种基于PSO优化HWFCM的快速水下图像分割算法

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.     

基于RRTConCon算法的船舶装配拆卸高斯采样路径规划

船舶舱室内的零部件在进行装配拆卸时,需要依据正确可行的路径进出装配体,通常采用路径规划的方法确定拆装路径。针对RRTConCon算法是采用随机采样的方法选取位姿点,在解决船舶舱室内狭窄通道的路径规划上效率不高的问题,提出了一种基于高斯采样的RRTConCon算法(RRTGaussion),采用高斯分布函数进行分区采样:在大的开阔区域设置较少的采样点,在复杂区域或狭窄通道设置较多的采样点,然后进行局部规划,找出拆装路径。通过在虚拟环境中对该算法进行仿真验证,结果表明,该算法在解决船舶舱室中狭窄通道的路径规划问题上效率高于RRTConCon算法。     

Power efficient formation configuration for centralized leader–follower AUVs control

Autonomous underwater vehicles (AUVs) have rapidly developed in the last few decades due to their autonomous properties in the investigation of an underwater environment. The goal of this paper is to develop a power efficient formation control for the cooperative motion of AUVs with a support vessel as a leader. In this paper, a kinematic algorithm for the joint motion of an AUV with a support vessel was developed and that algorithm was expanded for the formation of AUVs. The AUV yaw, surge and sway control loops were designed for that purpose. The complexing navigation system structure for the AUV was also developed. Simulation results demonstrated efficiency of the proposed kinematic algorithm for the joint motion of AUVs. Also, influence of lateral ocean current was considered. After development of the centralized leader?Cfollower formation control for the group of AUVs with a support vessel as a leader, we optimized a formation configuration in terms of power efficiency. Drag forces caused by AUV motion in the water can significantly influence power consumption. We investigated the relationship between the AUV's formation configuration, underwater coverage efficiency, communication quality and power consumption. As a result of research, we proposed a power efficient formation configuration for typical underwater operations. As a result, the effect of the AUV formation configuration on the power consumption was investigated and a trade-off solution for the optimal AUV positions in formation with minimal energy consumption, high coverage efficiency and small communication power consumption was derived.     

大范围环境下自主式水下潜器两种全局路径规划方法的研究

应用遗传算法(GA)和A·算法对自主式水下潜器(简称AUV)在大范围海洋环境中的全局路径规划问题进行了研究.介绍了基于栅格的环境模型及其数据结构,讨论了GA的染色体编码方式、基于知识的初始种群生成方法与适应度函数,基于领域知识设计了五种遗传算子,给出了A·算法的具体实现方法.通过仿真结果可以看出:GA采用可变长编码方式使路径描述简单、清晰,具有收敛速度快、求解实际问题效率高的特点;A*算法可在较短时间内求得相对栅格优化的路径.两种算法均可满足系统实时性要求.     

基于虚拟势场理论的AUV局部路径规划方法

针对自治式水下潜器（AUV）局部路径规划问题，基于虚拟势场理论研究，提出了虚拟力概念，并设计了一种改进型虚拟势场局部规划算法，解决了经典虚拟势场能算法中存在的零合力与U型障碍的局部极小问题：当AUV陷入局部极小时通过调用相应的逃避算法来摆脱局部极点。仿真结果验证了该算法对于克服虚拟势场能中存在的局部极点的有效性和可行性。     

自主式水下机器人最优路径规划问题的研究

路径规划是水下机器人实现自主航行的重要环节。根据自主式水下机器人的动力学性质，路径规划的特点以及实现智能行为的要求，采用基于案例的遗传算法，实现了自主式水下机器人最优路径规划。给出该方案的基本框架和算法，在基于案例类比的学习方法中引入模糊多属性综合决策的方法建立决策算子进行案例的匹配，在遗传算法中实际知识的指导，适当地改进遗传算子，加快搜索速度。仿真结果证明该路径规划方法能够取得较好的规划结果，使自主式水下机器人具有了一定的自主导航，自主避障和自主作业的能力。     

基于强化学习的自主式水下潜器障碍规避技术（英文）

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.