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Intel Research Day

英特尔研究日

  
发布日期: 2010-07-06
HERB the robot | 香草的机器人
香草的机器人 英特尔研究日
药草,居探索机器人巴特勒短,是一种实验室和英特尔之间的卡内基梅隆大学是2006年以来一直在进行的合作项目。草药是从一个老式的本意是要在老人或残疾人家庭使用赛格威的机器人。 

该机器人知道如何做事情,比如拿起瓶子放进回收站的人或他们手头的项目,但并不是因为它的被教导所有这些任务。相反草药是一个学习,自主机器人。 

该机器人“看到”物体的三维激光在其前面的模型,它使用扫描的东西。它发现对象 - 人类一样,瓶,回收箱,等等 - 并执行对计算机内部对什么都知道这些项目的任务。
HERB, short for Home Exploring Robot Butler, is a joint project between Intel Labs and Carnegie Mellon University that has been going on since 2006. HERB is a robot fashioned out of a Segway that is intended to be used in the homes of the elderly or disabled.

The robot knows how to do things like pick up bottles and put them in the recycle bin or hand items to people, but not because it's been taught all of these tasks. Rather HERB is a learning, autonomous robot.

The robot "sees" the 3D models of objects based on the laser on its front it uses to scan for things. It finds objects--like humans, bottles, recycle bins, and more--and executes tasks based on what the computer inside knows about those items.
Project Oasis | 项目绿洲
项目绿洲 英特尔研究日
项目采用深度相机绿洲解释像牛排和左合照甜椒现实世界的对象,他们创造三维交涉无特殊传感器或条形码的需要。 

深度相机可以拿起定期厨房柜台,牛排减去背景,使信息出现在以“按钮计数器。”当它看到牛排,一个按钮将预计在柜台上。挖掘虚拟按钮带来了像食谱菜单选项,购物清单等等。 

这个想法是建立一个“智能”的空间,不需要实验室或多个传感器的安装,就像做饭可以转化为现实世界的情况。
Project Oasis uses a depth camera to interpret real world objects, like the steak and bell pepper pictured at left, and create 3D representations of them without the need for special sensors or bar codes.

The depth camera can pick up the steak on a regular kitchen counter, subtract the background, and make information appear on the counter with "buttons." When it sees the steak, a button will be projected on the counter. Tapping the virtual button brings up menu options like recipes, shopping lists and more.

The idea is to create a "smart" space that doesn't require a lab or a setup of many sensors and can be translated to real-world situations, like cooking.
Oasis up close | 绿洲近距离
绿洲近距离 英特尔研究日
对项目绿洲近距离开枪。该系统可以识别两个对象 - 牛排和甜椒 - 并自动生成食谱包含在厨房的柜台上虚拟菜单预计这两个项目。
An up close shot of Project Oasis. The system can recognize the two objects--steak and bell pepper--and automatically generate recipes that contain the two items in a virtual menu projected on the kitchen counter.
Project Portico | 项目波蒂科
项目波蒂科 英特尔研究日
你如何使智能设备,工程不论位置?这就是英特尔正在与项目波蒂科在寻找。总部设在西雅图的研究实验室是在有形物体和手势识别寻找有用的应用。 

小相机连接到平板电脑可以拿起不只是什么人对平板触摸屏做,但他或她靠近片做。该相机基本上是扩大面积的片剂。 

其结果是,“如果你的设备是你周围的事物知道,它可以教你如何切丝胡萝卜或如何改变你的化油器,”安东尼说拉马尔卡,对项目的原则工程师。
How do you make a smart device that works regardless of location? That's what Intel is looking at with Project Portico. Research based in the Seattle lab is looking at useful applications of both physical objects and gesture recognition.

Small cameras attached to a tablet PC can pick up not only what a person is doing on the touch-sensitive screen of the tablet, but what he or she is doing near the tablet. The cameras are basically enlarging the surface area of the tablet.

The result is that "if your device is aware of the things around you, it could teach you how to julienne carrots or how to change your carburetor," said Anthony LaMarca, a principle engineer on the project.
Holodeck Car | 全息成像台上车
全息成像台上车 英特尔研究日
在全息成像台上的汽车项目已在工程约6在与南澳大学联合研究的努力个月。在全息成像台上车实现了全尺寸轿车,可以让之前曾因制造一个单一的模式进行微调工程师燃料的车辆和不同部位的三维投影安置效率。 

上一纸板真人大小模型的研究人员在澳大利亚的汽车项目的形象,坚持在一个风洞,在那里他们可以精简汽车和移动侧后视镜和大灯它周围找到最佳的汽车燃油效率,帕特麦卡利说,系统分析师在英特尔。其目的是削减成本,消除建设现实世界模型的浪费。
The Holodeck Car project has been in the works for about six months in a joint research endeavor with the University of South Australia. The Holodeck Car enables a 3D projection of a full-size car that can allow engineers to fine tune the fuel-efficiency of the vehicle and placement of different parts before ever having to manufacture a single model.

The researchers in Australia project the image of the car onto a cardboard life-size model and stick it in a windtunnel, where they can streamline the car and move side mirrors and headlights around to find optimal fuel efficiency for the car, said Pat McCulley, systems analyst at Intel. The aim is to cut costs and eliminate waste from building real-world models.