ROBOTMAK3RS Blog

This Checkout Scanner project I posted recently uses the NXT 2.0 Color Sensor in Light Sensor mode to scan simple "barcodes" made out of LEGO beams and imitate a laser barcode scanner. The interesting thing is how fast it is able to work (the standard light sensor would be as fast as well). Here is a video of the scanner in action: This project was inspired a bit by one of the missions on the FLL board this year, the Medicine Dispenser, where teams might want to use a light sensor to count black lines on the mat to get the exact distance right. My team was doing this mission and driving very slowly to make sure the sensor was able to count the lines properly. As I was watching them, I was thinking this was one of those cases where your instincts as a human don't relate well to what a robot can do. I knew the sensor could work much faster (and therefore the motor response is the limiting factor, not the sensor), but I was curious how much faster. So I did some tes...

Joey, a young fan of LEGO Mindstorms who runs his own blog, was recently invited to test Dexter's upcoming sensor.  He wrote a nice article about it . I invite you to read it on his LEGO-X blog latest post: Special! Datalog report for the new sensor from Dexter Ind. : dPressure The NXT Step is often his number one source for news and information about NXT related things ...but not this time!  Well done Joey and thanks for your report.

The list is here . Which of these things (or variations) have you done?

I've had the chance to have a good play around with the new education version of the software, in particular the datalogging aspect. Whilst it's possible to do datalogging in previous versions (check out some of Brian's amazing posts) the new version ties it all together in a nice GUI that I think will appeal to many teachers. Each experiment has it's own Configuration panel where you can specify the sampling rate, overall experiment length and the sensors you'll be using. Once setup, you can run the experiment in either Real-time or remote mode. Real-time allows you to see the data as it's captured whereas remote allows you to download the experiment, take the NXT to another location and upload the data once the experiment is finished. NXT-G2.0 produces some nice graphs and enables you to to do some basic analysis. This is the graph that was produced when I logged the light levels in a room for 24 hours. Overall I really like it. It is quick to setup and run...

This is a new curriculum by Carnegie-Mellon, designed to introduce students to data logging with NXT-G 2.0. Included are lesson plans and video tutorials for the following experiments: 1)Motion: Program a mobile robot to obey the posted speed limit 2)Heat: Investigate how effectively various materials block radiant heat 3)Sound: Investigate the sound sensor's response to different kinds of sounds 4)Light and Color: Investigate whether or not your robot can detect "rust" and "mold" in narrow "ventilation ducts" 5)Light: Program your robot's light sensor to detect structural weaknesses on the underside of a "bridge" The curriculum includes high-quality instructional videos, student worksheets, quizzes and an extensive teacher's guide. Purchase includes a classroom license. A VIDEO that features the motion experiment is here . BUILDING INSTRUCTIONS for the stand used in the sound investigation are here . An OVERVIEW of the cur...

While Brian overheats and I freeze to death, I'll show you an example of comparison data logging with the sound sensor and NXT-G 2.0. The faint, wavy yellow line is my prediction of what my voice would sound like (graphically speaking) if I counted to ten, speaking into the sound sensor that's connected to the brick in another room. (To be honest, I just drew a squiggly line across the screen, but we'll call it a prediction for purposes of contrast). The solid line with sharp peaks and valleys is the graphical depiction of my actual voice, rising as I speak and dropping off in the silences in between. As you can see, my "prediction" is woefully out of sync with my actual voice. Both the predictive and actual lines are overlaid for comparison. You can see the minimum, maximum and mean for each data set if you click the graphic.

Carnegie-Mellon has posted eight, flash-based videos on how to use the new data logging feature in NXT-G 2.0. The videos are here . (Image courtesy of CMU's "Robotics Academy" web site)