A programming assignment to introduce the ideas of probabilistic planning using the RRT and RRT* adaptions. A robotic arm (py_arm) is utilized to explore planning problems that can not be efficiently solved by deterministic planners such as A*.
You will need to install the python package py_arm. To do this, go to the py_arm repository and clone it into a directory. Then, from within that directory, run pip via the following command:
pip install py-arm
- astar_planner_arm.py A complete and functional A* planner that
is setup to interact with
py_arm. Accepts a command line argument so that you can create an arm with the desired degrees of freedom (DOFs) and run the A* planner. - rrt.py A template for building an RRT and RRT* planner (needs methods to be completed before it is functional). Once complete, a main method exists that will call RRT and RRT* on a straight line (2d) problem and illustrate the tree and solution path.
- rrt_problems.py Defines an abstract problem class that is used to define specific problem specific functions that are required by RRT.
- rrt_problems_py_arm.py Extends the problem class so support py_arm.
- test_rrt.py Unit tests for RRT and RRT*.
- spatial_map.py Implements methods for identifying nearest neighbors.
Once py_arm is installed, you can experiment with using A* to solve
planning problems with it. A command line argument, --dof, controls
the number of links. Caution: 3 links takes about 5 minutes for
A* to solve on my computer. So, testing your configuration with 2
links first is recommended, as shown in the following code block.
python astar_planner_arm.py --dof 2
spatial_map.py has functions in it to determine the nearest
neighbors, specifically the function within_delta