1. Implement random tree.
- derive from base::Planner, implement solve, clear, and getPlannerData (DONE)
- solve for exact solution if found (DONE)
- solve for approx path that ends in closest state to goal (DONE)

2. Create 2D environments (DONE)
- Go around
	- 2 squares with corners:
		- [(-0.5,0.5),(-0.5,-0.5),(-0.25,-0.5),(-0.25,0.5)]
		- [(0.25,-0.25),(0.25,-0.5),(-0.25,-0.5),(-0.25,-0.25)]
- Narrow path
	- 2 squares with corners:
		- [(-1.0,0.5),(-1.0,-0.5),(-0.25,-0.5),(-0.25,0.5)]
		- [(1.0,0.5),(1.0,-0.5),(0.25,-0.5),(0.25,0.5)]

3. Create exact collision checking methods
- point robot (DONE)
- circle robot radius = 0.1 (can change in plotsolution.py) (DONE)
- side length of square robot = 0.25 (can also change) (DONE)

4. Compute motion plans for different robot geometries using random tree.
- Visualize the world (DONE)

5. Compare random tree with PRM,EST,RRT using benchmark class
- need 20 independent runs for each planner (DONE)