Introducing GAs
Moving the robot arm requires the coordination of three motors simultaneously to create a smooth movement. We need a mechanism to create different combinations of motor movement for the robot to test. We could just use random numbers, but that would be inefficient and could take thousands of trials to get to the level of training we want.
What if we had a way of trying different combinations of motor movement, and then pitting them against one another to pick the best one? It would be a sort of Darwinian survival of the fittest for arm movement scripts – such as a GA process. Let’s explore how we can apply this concept to our use case.
Understanding how the GA process works
Here are the steps involved in our GA process:
- We do a trial run to go from position 1 (neutral carry) to position 2 (pickup). The robot moves the arm 100 times before getting the hand into the right position. Why 100? We need a large enough sample space to allow the...
