In this paper the path planning of AUV is introduced.
阐述了智能水下机器人的路径规划方法。
Firstly, the AUV communication protocol stack is analyzed.
本文首先对AUV通信协议栈进行了分析。
When AUV is working independently, it takes the SINS/DVL mode to navigate.
水下自主潜器工作时，水下定位采用SINS/DVL组合模式。
The application of layer protocol of CAN Bus is established for controlling AUV-VBS.
制定了CAN总线的应用层协议、并编制了相应的系统软件。
Autonomous underwater vehicle (AUV) is a multi-body system composed of a load and a carrier.
自主式水下航行器是由载荷和运载体组成的一个多体系统。
There is little information at present about the studies on maneuverability of AUV with load separation.
本文所要研究的是一种可实现载荷分离的特殊水下航行器。
Finally, results of lake test are given to validate the mission and task coordination method of the AUV planning layer.
最后，通过湖试试验验证了AUV规划层使命与任务协调方法的正确性。
Thus the Autonomous Underwater Vehicle (AUV), which serves as a platform for marine exploration, will become a hot research field.
其中自主式水下机器人（AUV）作为未来海洋探测开发的平台，有极其广阔的开发前景。
Using bionics theory, it is a research hotspot that people develop new bionic intelligent system of AUV by animating dolphin or tuna.
利用仿生学的原理，以某种鱼类或海豚等为研究对象，构成新型的仿生智能水下机器人平台。
This method is applied to the control subsystem of autonomous underwater vehicles (AUV) for detecting the failures of actuators or sensors.
在水下航行器控制系统中应用该方法对传感器和执行器的故障进行检测。
Autonomous Underwater Vehicle (AUV) is the platform of ocean monitoring instruments and the key equipment for obtaining ocean monitoring data.
水下自航行器(AUV)是海洋监测仪器的搭载平台，也是获取海洋监测数据的关键装备。
In this paper the necessity and importance that construct a automatic motion control Rule Base in automatic under vehicle (AUV) are addressed.
讨论并分析了建立水下无人潜器(AUV)自主运动控制规则库的必要性和重要性及可行性。
Since the difficulties of underwater communication among AUVs, collision avoidance of single AUV is designed based on improved potential field .
根据智能水下机器人（AUV）水下通讯相对困难的特点，在改进势力场法避障的基础上，实现了单机器人的避碰控制。
To gather the data, the team launched an Autonomous Underwater Vehicle (AUV) on six survey missions beneath the floating tongue of Pine Island Glacier.
为了收集数据，研究小组使用水下机器人(AUV)在松岛冰川浮动尖岬下面进行了6次调查作业。
This paper researched the ASIC design in the automation system of a kind of AUV with high-level design method based on VHDL high - level Synthesis.
本文采用基于VHDL高级综合的高层次设计方法对某型水下航行器自控系统的集成设计进行了研究。
As a new kind of autonomous underwater vehicle (AUV), an underwater glider has valuable applications in oceanographic survey or in ocean exploration.
水下滑翔器作为一种新型水下机器人系统，对于海洋环境监测与资源探测具有重要应用价值。
Essential characteristic of AUV which is understood completely and intelligence of AUV which is improved are crucial to the study and the application.
正确认识水下机器人的本质特征，提高水下机器人的智能化水平具有重要的理论和现实意义。
To improve the navigation accuracy of an autonomous underwater vehicle (AUV), a novel terrain passive integrated navigation system (TPINS) is presented.
为了提高自主水下航行器的导航精度，提出了一种新型地形无源组合导航系统。
Since the demand for autonomous underwater vehicle (auv) "s voyage ability has improved, how to solve the navigation precision has became a key problem".
随着对自主水下机器人远距离航行能力要求的不断提高，如何提高水下机器人长航导航精度成为亟待解决的问题。
"The AUV missions have given us a real insight into the nature of Antarctic sea ice - like looking through a microscope," said co-author Jeremy Wilkinson.
“水下机器人的任务给了我们真正体会到南极海冰的性质-喜欢看通过显微镜，说：”合着者杰里米·威尔金森。
In the paper, with the characteristics of AUV dynamic model is concerned, total least squares (TLS) is chosen to identify the hydrodynamic parameters of AUV.
考虑到AUV运动模型的特点，本文提出用全局最小二乘法(TLS)来辨识AUV水动力参数。
While the requests of AUV design performance are met, the objectives of minimizing gross weight and maximizing propeller power and payload length are achieved.
在满足AUV的设计性能要求的同时，实现了使得AUV总重量和推进功率最小，有效载荷段的长度最大等目标函数。
Considering the effect of ocean current on AUV, the paper designs two planning methods of identification as reconnaissance and identification after reconnaissance.
针对海流对水下机器人运动的影响，设计了边探测边识别和先探测后识别两种探测方法。
The autonomous underwater vehicle (AUV) is a complex intelligent electromechanical system to carry out different types of missions in the complex ocean environment.
自主式水下航行器是一个复杂的智能化机电系统，是能够在复杂的海洋环境中自主执行各种任务的无人平台。
The reinforcement learning is adopted to control and decision for AUV, and Q-learning, BP neural net, artificial potential is integrated to avoidance planning for AUV.
主要采用强化学习的方法对AUV进行控制和决策，综合Q学习算法、BP神经网络和人工势场法对AUV进行避碰规划。
Because the considerations of effect of ocean current on AUV and the corresponding methods, the abilities of autonomous planning and motion control of AUV are improved.
本文在规划的各阶段均考虑了海流对水下机器人运动的影响，并采取了相应的规划策略，提高了水下机器人的自主规划能力和运动控制性能。
The motion of Automous Underwater Vehicle (AUV) changes obviously when it sails near surface under the influence of ocean waves, so it is difficult to sustain the depth.
智能水下机器人（AUV）在近水面的运动中，由于波浪干扰力的作用，运动状态会发生明显变化，其近水面的定深控制是比较困难的。
The task coordination algorithm was tested according to a terrain scanning scenario in a virtual system and demonstrated that an AUV can fulfill its mission autonomously.
最后在虚拟仿真平台上结合地形勘察案例验证了任务调度算法，结果证明AUV能够在无人干预下自主地完成整个使命。
The task coordination algorithm was tested according to a terrain scanning scenario in a virtual system and demonstrated that an AUV can fulfill its mission autonomously.
最后在虚拟仿真平台上结合地形勘察案例验证了任务调度算法，结果证明AUV能够在无人干预下自主地完成整个使命。
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