Organised chaos gets robots going - 01 November 2004 - New Scientist

Description

A control system based on chaos has made a simulated, multi-legged robot walk successfully. The researchers behind the feat say it may have brought us closer to understanding how people and animals learn to move.

Standard robots control their leg motion either through complex computer programs or by using so-called genetic algorithms to "evolve" a successful walking strategy. Both these options are time-consuming and require a lot of computer power.

Roboticists Yasuo Kuniyoshi and Shinsuke Suzuki wondered whether chaotic systems might also generate efficient walking behaviour. Chaotic systems behave in a way that means that small effects are amplified so rapidly that the systems' behaviour becomes impossible to predict more than a short time ahead. Such chaotic systems are behind a number of phenomena, including the weather and the performance of financial markets.

The Tokyo University pair reasoned that just as the chaotic maths that determines the weather can produce clear patterns such as hurricanes and weather fronts, similar systems might underlie the movement patterns involved in locomotion. "We, and animals, seem to be able to work out how to move in different situations without going through thousands of trial-and-error situations like today's robot-control software does," says Kuniyoshi.

To test their idea, Kuniyoshi and Suzuki devised a computer simulation of a 12-legged machine in which each leg was controlled by a chaotic mathematical function. The functions were initially fed 12 parameters chosen at random. From then on, sensory information from each limb was fed back into the chaotic function that controlled it.

Going nowhere

The team found that certain combinations of starting parameters made the robot's limbs rapidly adopt "walking-on-the-spot" behaviour, but the machine did not get anywhere. However, when they placed a weight at one end of the simulated robot (see graphic) they found that four of the ...

Original URL

http://www.newscientist.com/article/dn6582

http://www.newscientist.com/article/dn6582