This repo provides a Python implementation of the Ant Colony Optimization Algorithm for path planning purposes. The ant colony optimization algorithm implemented in this repo is the Ant System Algorithm.
The package is made up by two directories:
-
The aco directory: contains the ant_colony class for handling the ant colony optimization algorithm and the map_class class for handling the operations with the map.
-
The maps directory: contains the maps to calculate the path from.
The user will pass to the program the number of iterations, ants,map, p,Q and if the map and result is displayed graphically or not.
- p: is the pheromone's evaporation rate, [0-1]
- Q: is the pheromone adding constant.
Information regarding how the program is executed is displayed when executing from the command line: python aco_resolve_path.py --help
The maps have to be included in the maps directory. For a map to be valid, it must meet the following requirements:
- mxm map, square matrix shape.
- S indicates the starting point, just one starting point is allowed.
- F indicates the final point, just one final point is allowed.
- E indicates if a point in the map is empty.
- O indicates if a point in the map is occupied.
A resulting path would be the following:
