I have a 2000 pounds real life robot with two arms. The arms are controlled by 15 motors and gets position feedback from 15 potentiometers. The robot is placed in a room with stationary objects which positions are known. The motors are only able to move with full speed.
Get the two arms of the robot from their current point to a selected point.
My current solution:
Currently the robot is moving by a big handmade table, but the problem is that if an object is moved the robot is going to collide. And it is almost impossible to imagine all possible situations.
I have created a 3D simulation for the robot which I am planning to use when training the robot. The 3D simulation can detect collision with objects and between the two arms.
First I looked at some path finding algorithms like A* but with 15 motor in a 3D environment it is not very useful. Then I begin to look at neural network and Q-learning but I am not sure it is the right way to go. I think I need some kind of reinforcement learning.
My input and output could be something like:
15 x Current motor pos
15 x Goto pos
Obejects in room pos or the 3D simulation
15 x (maybe 5 states) sub positions for the motors
I am currently trying to figure out which network and learning method to use if I go with neural network and reinforcement learning.
Is it doable?
I hope someone can help me and maybe also point to an example.
Thank you in advance.
With best regards,
FuzsyMember Since 07 Sep 2012
Offline Last Active Mar 08 2016 07:34 AM
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