Optimal Path finding A.I.

This project was a test to get my feet wet with some practical applications using Artificial Intelligence. The task is (relatively) simple: There's a simulated warehouse environment with agents who's function is to acquire items from a shelf and deliver them to a drop off location. There's a starting/resting location and the agents need to be as efficient as possible in acquiring their item.



Why is efficiency necessary?

Maximizing efficiency will insure that the items are being moved as ideally as possible, minimizing energy consumption from the agents and also time needed to have everything moved to the designated area. If the warehouse is being accessed rapidly and multiple items need to be moved for transportation, then having the agents stumble around and wasting time isn't going to be beneficial to the owners of the warehouse.

The project was divided into 4 different scenarios:
  - 1 agent
  - 2 agents
  - 3 agents
  - 3 agents with "random" starting/resting positions

Where each agent has a designated shelf to access in the warehouse where they need to pick up their items to deliver them to the designated drop off location. The drop off location would be a simulated area where the items would then be loaded into a transport vehicle of some sort.



Furthermore, when there are multiple agents running around in the warehouse, they can't be running into each other.

For more information and the actual files and code, visit the GitHub repo for the project HERE!