Boundary generation occurs in the environment class within src/boundary_detection.py
The class works by setting an ego agent (the pupper), adding each detected fiducial as an obstacle, and if a fiducial with id = 0 was detected then a goal object is added to the class as well
Obstacle locations are updated if a fiducial with an existing id is detected and obstacle locations can be cleared with clear_obstacles
Then a list of boundaries corresponding to each obstacle is created using the [Minkowski Sum](https en.wikipedia.org/wiki/Minkowski_addition) formula
Configuration space is essentially a series of points that represent infeasible locations and rotations for a robot in an environment
We can use this along with discretization to generate a graph of all the feasible locations for the pupper robot in an environment with obstacles, assuming we have the models for the agent and the obstacles
Below are two example configuration spaces shown in red for an ego agent in green for the obstacle in blue, notice how the configuration spaces changes when the rotation of the agent changes
