The robot appears to be dragging the victim up the ramp by his/her neck.

Very Nice!

This was #8 in a series of photos from a Boston.com News in Photographs piece.   The caption there reads:

Tokyo Fire Department’s rescue robot transfers a mock victim onto itself during an anti-terrorism exercise in the response to a radiological dispersal device in Tokyo, on November 7, 2008. Tokyo Metropolitan government conducted the exercise with eleven organisations including Metropolitan Police Department. (TOSHIFUMI KITAMURA/AFP/Getty Images)

It took the genius of Charles Darwin to break with the common belief that species were constant and unchanging.  His voyage on the Beagle was essential in forming these ideas as he encountered fossils of extinct forms and the effects of dramatic earthquakes in the Andes, and was led to realize that the Earth changes and that species change.  This idea of long-term change is hard to come by.  It even evaded Albert Einstein when he derived the expansion of the universe from his theory of general relativity and decided to introduce a correction term in the mathematics to ensure that the universe was constant as he knew it to be.  Einstein called this his biggest blunder.  Yet it was exactly this belief of constancy that Charles Darwin was able to shed.

What few people realize is that Western capitalism has embraced Darwinism and use the arguments of survival of the fittest to defend deregulation in the market.  This competition does work well… as long as you actually have competition.

In the area of data analysis and machine learning, which are my areas of expertise, we rely heavily on computer algorithms that search vast high dimensional spaces for solutions to problems.  The best algorithms employ techniques that are central to evolution.  These methods are called Markov chain Monte Carlo (MCMC)  techniques, and in some specific cases these algorithms have direct relation to genetic evolution and are called genetic algorithms.  What makes these algorithms work is precisely what makes evolution work.

These algorithms rely on a fitness function that enables us to measure the fitness of a hypothesized solution to a problem (we call this a sample).  We often start with many samples scattered throughout the space and let them explore via mutations.  In some algorithms, we take good samples and duplicate them and let them explore further, or we may even combine characteristics of a pair of samples to create a new one (as in genetic algorithms).  After hundreds of thousands of iterations, the algorithms are able to find the solutions to the problem.  These solutions would be impossible to find via brute force search or guessing.

The analogy that holds here is that of organism as sample.  Anyone who has actually done these simulations can understand that you can obtain solutions worthy of creation simply by iterating hundreds of thousands of times under the force of a selection pressure.

Happy Birthday Charles Darwin, and thank you for the insights that have advanced machine learning in the last two decades.

Kevin Knuth
Albany NY

UAlbany Professor Kevin Knuth with a robot built from LEGOs. (Photo Mark Schmidt)

Kevin Knuth has a laboratory in the physics department of the University at Albany that is filled with LEGOs. The bricks are relatively cheap and can be used to rapidly prototype a robot’s body. Knuth’s robots are being programmed to solve such problems as mapping complex terrain.

At UAlbany Day on Saturday, Oct. 25, he will give a demonstration on Robotics and Robotic Exploration in Life Sciences Room 143 at 10:45 a.m.

More here:

http://www.albany.edu/news/update_4522.shtml

Building instructions for the robot shown in the UAlbany article can be found on Brickengineer.com

Visit Autonomous Exploration News for information on Knuth’s company Autonomous Exploration Inc.

Visit Robots Everywhere for a general blog on robotics news.

I have just launched a new blog that focuses entirely on advances in robotics.

This blog will provide information about research-quality advances and links to the more technical aspects, such as journal papers, computer code, and other resources.

Please visit Robots Everywhere!

Kevin Knuth
Albany NY

At makezine.com, I stumbled on this gem of a book titled “Making Things Talk”

It is packed with 26 electronics projects that involve getting these tiny computers to talk to each other and the internet over both wired and wireless connections.  I have ordered all the requisite parts, and when purchased, they amount to around $280.  I am looking forward to summer vacation when I get to go through each of these projects one-by-one:

1. Making a computer “mouse” out of a stuffed animal monkey and flex sensors.
2. Making the monkey wireless
3. Negotiating in Bluetooth with the BlueSMiRF module
4. Setting up a networked webcam
5. Connecting a microcontroller to the internet without a computer
6. Networked Air Quality Meter
7. Networked Games
8. Infrared Communication
9. Radio Communication
10. Duplex Radio Communication
11. Bluetooth Communication
12. Broadcasting Messaging
13. Directed Messaging
14. Infrared Rangefinding
15. Ultrasonic Rangefinding
16. Reading Signal Strength with XBee Radios
17. Reading Signal Strength with Bluetooth Radios
18. Reading the GPS Serial Protocol
19. Heading with a Digital Compass
20. Attitude with an Accelerometer
21. Color Recognition with a Webcam
22. 2D Barcode Recognition with a Webcam
23. Reading RFID Tags
24. RFID and Home Automation
25. IP Geocoding
26. Email from RFID

OK, I wont be making each of these.  I will get an idea halfway through and take off and work on that.  But it should be fun!

Kevin Knuth
Albany NY