Distributed-Indoor
所属分类：GO语言编程
开发工具：GO
文件大小：8788KB
下载次数：1
上传日期：2020-04-29 01:43:06
上 传 者：
羔羊GG
说明： 分布式网络室内定位仿真，移动网络节点定位。go语言写的，多移动目标的定位技术，
(Distributed network indoor positioning simulation, mobile network node positioning. written in go language, positioning technology for multiple moving targets,)
文件列表:
Proposal (0, 2019-03-16)
Proposal\Project 2 Final Proposal.pdf (495008, 2019-03-16)
SendAzure (0, 2019-03-16)
SendAzure\AzureListener (8890660, 2019-03-16)
SendAzure\AzureListener.go (2620, 2019-03-16)
SendAzure\LocalListener.go (2177, 2019-03-16)
SendAzure\map-render (0, 2019-03-16)
SendAzure\map-render\package-lock.json (457007, 2019-03-16)
SendAzure\map-render\package.json (686, 2019-03-16)
SendAzure\map-render\public (0, 2019-03-16)
SendAzure\map-render\public\favicon.ico (3870, 2019-03-16)
SendAzure\map-render\public\index.html (1590, 2019-03-16)
SendAzure\map-render\public\manifest.json (317, 2019-03-16)
SendAzure\map-render\src (0, 2019-03-16)
SendAzure\map-render\src\App.css (689, 2019-03-16)
SendAzure\map-render\src\App.js (2981, 2019-03-16)
SendAzure\map-render\src\App.test.js (248, 2019-03-16)
SendAzure\map-render\src\DataGrid.js (1470, 2019-03-16)
SendAzure\map-render\src\FixedBox.js (386, 2019-03-16)
SendAzure\map-render\src\HeatMap.js (902, 2019-03-16)
SendAzure\map-render\src\XLabels.js (375, 2019-03-16)
SendAzure\map-render\src\index.css (63, 2019-03-16)
SendAzure\map-render\src\index.js (254, 2019-03-16)
SendAzure\map-render\src\logo.svg (2671, 2019-03-16)
SendAzure\map-render\src\registerServiceWorker.js (4384, 2019-03-16)
govector_playground.go (2283, 2019-03-16)
gpio (0, 2019-03-16)
gpio\LICENSE (1486, 2019-03-16)
gpio\io.go (2007, 2019-03-16)
gpio\select_darwin.go (273, 2019-03-16)
gpio\select_linux.go (313, 2019-03-16)
gpio\sysfs.go (3877, 2019-03-16)
gpio\watcher.go (5736, 2019-03-16)
main (8780100, 2019-03-16)
... ...
Introduction:
The objective of this project is to build a distributed system in which the peer robts will work
together to map out the area they are in. Each robot will communicate its finding with its
neighbours only when they come within a certain range of other robots within an Ad-hoc network. The robots will
exchange their maps, resolve any conflicts between these maps and its local one, and
coordinate how to explore the undiscovered areas. At the same time, robots will upload the map in consensus to the server(for display only) in the Azure cloud to display the result.
近期下载者：
相关文件：
收藏者：