Experimenting with an evolutionary solver (Galapagos) to find solutions for a 6-axis robot.

--> Input: The Genome is made up of the 6 angle values (each with a different range) for the 6 axes.

--> Fitness Value: I tested a few different combinations - initially trying to minimise difference in X,Y,Z Vectors for the Robot Plane and Plane to Align With, and minimise Distance between the 2 Plane origins.

In the video shown, I've used minimise distance between the end points of the 3 Vectors and minimise origin distance. This has the advantage of making all the fitness values in the same units (mm, cm, etc) rather than needing to combine angles and lengths.

I was surprised with how good a result Galapagos found. The angle of each axis affects those further along the robot 'arm', therefore a good solution for the final 5 axes can still appear to be a poor one just because the 1st axis value is far out. As a result I think this problem is one where good solutions are hidden in 'noise' and hard to distinguish.

See David Rutten's discussion for more on this:

http://ieatbugsforbreakfast.wordpress.com/2011/03/04/fitness-functions/

So Galapagos isn't really the right tool for this -- far better to just determine the exact solution using inverse kinematics (i.e. Lobster, et all) -- but it's fun testing its limits.

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Utilizes Galapagos, by David Rutten.

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