Localization And Communication Of Underwater Relay Node

Underwater exploration which owns vast industry potentions needs the assistance of underwater relay network.Unmanned Autonomous Vehicles(UAV)can be a perfect candicate for relay network.The UAV,with great market potentions,has become more popular with the development of technology and reduction of cost.However,the attenuation properties of underwater electromagnetic wave limits the wireless communication of UAV.Aiming at wireless communicating of UAVs,we pay our attention on underwater locating and communication.We begin with locating algorithm which is regarded as the infrastructure of underwater system.While traditional acoustic locating method needs the pre-known information such as sound speed,we introduce a method which needs no pre-known infomation such as sound speed.The accuracy of our method is superior to most methods exist like ToA,TDoA.Considering the refraction of sound wave in deep water,we introduce a searching method which has 15% precision improvement.We apply SIFT and ORB algorithm of extract the characters of UAV,and compare the characters with inputting video frames.These computer vision algorithms can give relative location of UAV.We choose optic waves as communication media after comparing the data rates,spread ranges among acoustic,electromagnetic waves and optic.Due to the huge directionality of optic,optical communication or visible light communication(VLC)will face beam steering problem.By introducing RTS/CTS mechanisms from IEEE 802.11 protocol,whose 'request-agree-transmit-acknowledge' mode is very similar to 'explorefeedback-update' mode of an episode of reinforcement learning,we designed a directional MAC protocol.The mathematical model has good convergence and robustness.Comparing to beam training method of 802.11 ad,our DMAC protocol has less communication overhead in hostile environment.The advantage over other deep learning methods(like ANN)is that our algorithm trains model with communication results rather than pre-labeled data.In order to have a better comprehension of the basic mechanism of UAVs,we buy an open-source UAV named OpenRov.We make a comprehensive analyze on Openrov by reading its source code.A series of UMLs are drew and uploaded to communities.We also write a set of Python scripts to control the UAV automatically.