Mapping Interior Spaces With Robots And GIS
Roland Piquepaille writes "In an article about GIS and Robotics, Directions Magazine reports that architects and other professionals can now use spatially intelligent robots to collect interior space data. With such mapping robots, it's possible to capture accurate data for over 10,000 square meters per day and to easily integrate it with existing software. The article doesn't mention the sources for its illustrations about these robotic systems, so I thought I'd point them out: a company in Maine called Penobscot Bay Media. You'll find more details and pictures about these mapping robots at ZDNet."
...was in colonoscopies, but all of the patients so far have died of massive internal bleeding.
Coralized so as not to /. their servera tial-Robotics.wmv
15 MB http://www.penbaymedia.com.nyud.net:8080/demos/Sp
[Fuck Beta]
o0t!
Can I have my home as a CS:Source map now, please ?
Seriously though, I don't see many uses for this isolated tech. It is, however, necessary to have something like this in 'intelligent' robots.
Because modern mobile robots are only minimally similar to Shakey-- the algorithms which make mapping and localization possible are statistical, rather than logical, and Shakey was logic-based system. Furthermore, Shakey wasn't a whole lot more than a physical incarnation of a blocksworld agent. In a sense, all modern mobile robots are distantly related to Shakey-- but only in the same sense as they're distantly related to Rodney Brooks' subsumption architecture robots. I'm surprised this article is coming up as news; robots capable of mapping and localization tasks have been around for several years now, and there's a great deal of off the shelf software (open source and otherwise) capable of this.
As a small semestrial academic project, I worked on a different kind of mapping project which uses a large number of very simple (and cheap) robots instead of a small number of expensive robots like in this article.
Each robot is aware of its location through odometry (measuring the distance traveled by both the of the bot's wheels) and collision detection using, in our case, a rotating straw due to the fact we were limited to Lego Mindstorms.
Using odometry inserts a lot of error to the calculations. To counter these errors, the robots communicate over a short distance (touching distance) and average their expected location and heading.
In theory, and simulation, the algorithm proved very successful. Especially for a large number of agents.
In practice the errors were too large compared to the very small number of agents (4) we had at our disposal.
The project page.
And the simulation applet, written with NetLogo.
I wonder if they use such averaging algorithms with these robots aswell.
^_^