my question is related to Neural Network architectures and how to handle a specific situation. Please bear with me, I'm studdying this by myself, reading books, taking online courses and just got to this situation:
I have a simple project where a vehicle moves around on a 2D plane. This vehicle must avoid collisions with other objects while it moves from one place to another.
I first designed an "avoidance" behaviour with vectors/forces -based on Reynolds work. This routine I made will return the direction and length of the vector the vehicle should take to avoid a single object. Then I generated data from different random scenarios and trained a neural network with it.
This seems to workd great!
This are my INPUTS and OUTPUTS:
Inputs: [vehicle_x, vehicle_y, vehicle_velocity_x, vehicle_velocity_y, obstacle_x, obstacle_y, obstacle_velocity_x, obstacle_velocity_y] Outputs: [ x, y, length ]
The problem here is that I can only work with one obstacle at a time and if I wanted to take a different decision for special scenarios I can't. For example, if many obstacles are near each other by a certain distance I would like to treat them as a "group" and calculate angle and force in a slighlt different manner.
So, the question is, what architecture or approach can I use to be able to feed a network with one or more obstacles?
Thanks in advance!