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Unified command and control for heterogeneous marine sensing networks

机译:异构海洋传感网络的统一指挥和控制

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摘要

Successful command and control (C2) of autonomous vehicles poses challenges that are unique to the marine environment, primarily highly restrictive acoustic communications throughput. To address this, the Unified C2 architecture presented here uses a highly compressed short message encoding scheme (Dynamic Compact Control Language or DCCL) to transfer commands and receive vehicle status. DCCL is readily reconfigurable to provide the flexibility needed to change commands on short notice. Furthermore, operation of multiple types of vehicles requires a C2 architecture that is both scalable and flexible to differences among platform hardware and abilities. The Unified C2 architecture uses the MOOS-IvP autonomy system to act as a “backseat driver” of the vehicle. This provides a uniform interface to the control system on all the vehicles. Also, a hierarchical configuration system is used to allow single changes in configuration to propagate to all vehicles in operation. Status data from all vehicles are displayed visually using Google Earth, which also allows a rapid meshing of data from other sources (sensors, automatic identification system, radar, satellites) from within, as well as outside of, the MOOS-IvP architecture. Results are presented throughout from the CCLNET08, SQUINT08, GLINT08, GLINT09, SWAMSI09, and DURIP09 experiments involving robotic marine autonomous surface craft (ASCs) and Bluefin, OceanServer, and NATO Undersea Research Centre (NURC) autonomous underwater vehicles (AUVs).
机译:自动驾驶车辆的成功指挥与控制(C2)带来了海洋环境特有的挑战,主要是限制性很高的声音通信吞吐量。为了解决这个问题,此处介绍的Unified C2体系结构使用高度压缩的短消息编码方案(动态紧凑控制语言或DCCL)来传输命令并接收车辆状态。 DCCL易于重新配置,以提供在短时间内更改命令所需的灵活性。此外,多种类型车辆的运行需要C2架构,该架构既可扩展又可灵活适应平台硬件和能力之间的差异。 Unified C2体系结构使用MOOS-IvP自治系统充当车辆的“后座驾驶员”。这为所有车辆上的控制系统提供了统一的接口。另外,使用分级配置系统来允许配置的单个更改传播到运行中的所有车辆。使用Google Earth可视化显示所有车辆的状态数据,它还可以快速划分来自MOOS-IvP体系结构内部和外部的其他来源(传感器,自动识别系统,雷达,卫星)的数据。整个CCLNET08,SQUINT08,GLINT08,GLINT09,SWAMSI09和DURIP09实验都展示了结果,这些实验涉及机器人海洋自动水面航行器(ASC)和Bluefin,OceanServer和北约海底研究中心(NURC)水下自动航行器(AUV)。

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