Willow Garage's PR2 Robots Graduate: "Eleven robots worth more than US $4 million head out to universities and research labs around the world
viernes, 28 de mayo de 2010
miércoles, 26 de mayo de 2010
Asimo can run.
Asimo can run.: "
Asimo the robot at the Museum of Emerging Science in Tokyo.
Cast: Derek Koch
"Asimo can run. from Derek Koch on Vimeo.
Robots: The Nao Humanoid
Robots: The Nao Humanoid: "
We've already reported on French company Aldebaran's Nao
in a previous post.
Nao has since grown up and made it into the RoboCup Standard
Platform League. The latest episode of the Robots podcast interviews Luc
Degaudenzi, Aldebaran's Vice President in Engineering, and his colleague
Cédric Vaudel, who is Aldebaran's Sales Manager for North America. In
addition, and as a premiere on the Robots Podcast, we also interview a
robot. Nao introduces himself and and then shares his own version of
Star Wars. Read
more or tune
in!
lunes, 24 de mayo de 2010
robotik robotlar
robotik robotlar: "
çeşitli robot uygulamaları robot kol lego
Cast: gevv
"robotik robotlar from gevv on Vimeo.
jueves, 20 de mayo de 2010
The Conference Room That Re-Arranges Itself
The Conference Room That Re-Arranges Itself: "Just pick how you want it set up and the tables move themselves into position
"
You can add a new entry to the long list of problems that can be solved by robots: arranging tables in a conference room. On my personal workplace hassle scale, I'm not sure that moving conference room furniture ranks much above "occasional nuisance." But Yukiko Sawada and Takashi Tsubouchi at the University of Tsukuba, Japan, evidently find shoving tables to be an unappealing task for humans. So they built a room that could re-arrange itself.
In this case, the tables are the robots. Select the arrangement you want from a graphical interface, and the tables will move to their new locations. The movement is monitored by an overhead camera with a fish-eye lens, and the software uses a trial-and-error approach to determine the best sequence of motion. But it's best to see the room in action for yourself. Check out the video the researchers presented at ICRA earlier this month.
In the paper, the authors explained the rationale for the project:
In these days, at conference rooms or event sites, people arrange tables to desired positions suitable for the event. If this work could be performed autonomously, it would cut down the man power and time needed. Furthermore, if it is linked to the Internet reservation system of the conference room, it would be able to arrange the tables to an arbitrary configuration by the desired time.
I'm not sure the cost and complexity of such a system could ever be low enough to be practical, but there's definitely something fun about watching the tables reconfigure themselves. And if you already have autonomous, why not go all the way and add a reconfigurable wall?
"You can add a new entry to the long list of problems that can be solved by robots: arranging tables in a conference room. On my personal workplace hassle scale, I'm not sure that moving conference room furniture ranks much above "occasional nuisance." But Yukiko Sawada and Takashi Tsubouchi at the University of Tsukuba, Japan, evidently find shoving tables to be an unappealing task for humans. So they built a room that could re-arrange itself.
In this case, the tables are the robots. Select the arrangement you want from a graphical interface, and the tables will move to their new locations. The movement is monitored by an overhead camera with a fish-eye lens, and the software uses a trial-and-error approach to determine the best sequence of motion. But it's best to see the room in action for yourself. Check out the video the researchers presented at ICRA earlier this month.
In the paper, the authors explained the rationale for the project:
In these days, at conference rooms or event sites, people arrange tables to desired positions suitable for the event. If this work could be performed autonomously, it would cut down the man power and time needed. Furthermore, if it is linked to the Internet reservation system of the conference room, it would be able to arrange the tables to an arbitrary configuration by the desired time.
I'm not sure the cost and complexity of such a system could ever be low enough to be practical, but there's definitely something fun about watching the tables reconfigure themselves. And if you already have autonomous, why not go all the way and add a reconfigurable wall?
martes, 18 de mayo de 2010
Explained: Monte Carlo simulations
Explained: Monte Carlo simulations: "Speak to enough scientists, and you hear the words “Monte Carlo” a lot. “We ran the Monte Carlos,” a researcher will say. What does that mean?
The scientists are referring to Monte Carlo simulations, a statistical technique used to model probabilistic (or “stochastic”) systems and establish the odds for a variety of outcomes. The concept was first popularized right after World War II, to study nuclear fission; mathematician Stanislaw Ulam coined the term in reference to an uncle who loved playing the odds at the Monte Carlo casino (then a world symbol of gambling, like Las Vegas today). Today there are multiple types of Monte Carlo simulations, used in fields from particle physics to engineering, finance and more.
To get a handle on a Monte Carlo simulation, first consider a scenario where we do not need one: to predict events in a simple, linear system. If you know the precise direction and velocity at which a shot put leaves an Olympic athlete’s hand, you can use a linear equation to accurately forecast how far it will fly. This case is a deterministic one, in which identical initial conditions will always lead to the same outcome.
The world, however, is full of more complicated systems than a shot-put toss. In these cases, the complex interaction of many variables — or the inherently probabilistic nature of certain phenomena — rules out a definitive prediction. So a Monte Carlo simulation uses essentially random inputs (within realistic limits) to model the system and produce probable outcomes.
In the 1990s, for instance, the Environmental Protection Agency started using Monte Carlo simulations in its risk assessments. Suppose you want to analyze the overall health risks of smog in a city, but you know that smog levels vary among neighborhoods, and that people spend varying amounts of time outdoors. Given a range of values for each variable, a Monte Carlo simulation will randomly select a number within each range, and see how they combine — and repeat the process tens of thousands or even millions of times. No two iterations of the simulation might be identical, but collectively they build up a realistic picture of the population’s smog exposure.
“In a deterministic simulation, you should get the same result every time you run it,” explains MIT computer science professor John Guttag in his OpenCourseWare lecture on Monte Carlo simulations. However, Guttag adds, in “stochastic simulations, the answer will differ from run to run, because there’s an element of randomness in it.”
The aggregation of data makes it possible to identify, say, a median level of smog exposure. To be sure, Monte Carlo simulations are as good as their inputs; accurate empirical data would be necessary to produce realistic simulation results.
"
The scientists are referring to Monte Carlo simulations, a statistical technique used to model probabilistic (or “stochastic”) systems and establish the odds for a variety of outcomes. The concept was first popularized right after World War II, to study nuclear fission; mathematician Stanislaw Ulam coined the term in reference to an uncle who loved playing the odds at the Monte Carlo casino (then a world symbol of gambling, like Las Vegas today). Today there are multiple types of Monte Carlo simulations, used in fields from particle physics to engineering, finance and more.
To get a handle on a Monte Carlo simulation, first consider a scenario where we do not need one: to predict events in a simple, linear system. If you know the precise direction and velocity at which a shot put leaves an Olympic athlete’s hand, you can use a linear equation to accurately forecast how far it will fly. This case is a deterministic one, in which identical initial conditions will always lead to the same outcome.
The world, however, is full of more complicated systems than a shot-put toss. In these cases, the complex interaction of many variables — or the inherently probabilistic nature of certain phenomena — rules out a definitive prediction. So a Monte Carlo simulation uses essentially random inputs (within realistic limits) to model the system and produce probable outcomes.
In the 1990s, for instance, the Environmental Protection Agency started using Monte Carlo simulations in its risk assessments. Suppose you want to analyze the overall health risks of smog in a city, but you know that smog levels vary among neighborhoods, and that people spend varying amounts of time outdoors. Given a range of values for each variable, a Monte Carlo simulation will randomly select a number within each range, and see how they combine — and repeat the process tens of thousands or even millions of times. No two iterations of the simulation might be identical, but collectively they build up a realistic picture of the population’s smog exposure.
“In a deterministic simulation, you should get the same result every time you run it,” explains MIT computer science professor John Guttag in his OpenCourseWare lecture on Monte Carlo simulations. However, Guttag adds, in “stochastic simulations, the answer will differ from run to run, because there’s an element of randomness in it.”
The aggregation of data makes it possible to identify, say, a median level of smog exposure. To be sure, Monte Carlo simulations are as good as their inputs; accurate empirical data would be necessary to produce realistic simulation results.
"
VEX Robotics World Championship Report
VEX Robotics World Championship Report: "
This year's VEX
Robotics World Championship was bigger than ever with more than 400
high school and university teams from around the world. It was held in
Dallas, Texas again this year, so you can be sure we were there. The
local Dallas Personal Robotics Group
also got in on the act, offering their members as volunteers to help with
the event. I managed to avoid serving as a judge this year, so I had more
time to take photos,
several of which you can see below. Grant Imahara
awarded the top prizes to China's Shanghai’s Luwan team and New
Zealand's Free Range Robotics and Kristin Doves teams. To make things
even crazier, the BEST National
Championship was held alongside the VEX events.
The Metro Homeschool team 229 from Blue Springs, Missouri took the first
place BEST award. Read on for more photos and details from the official
VEX press release or check out my almost 300
photos of the event.
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