My project: 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.
Task: 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.
Environment: 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.
My question: 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: Input: 15 x Current motor pos 15 x Goto pos Obejects in room pos or the 3D simulation
Output: 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.