Course work for: Udacity's Intro To Self-Driving Cars Nano Degree Program My implementation of the route planner project, using the A-Star search algorithm.

Inputs:

• map - (dict) {node_id: {'pos': (x_pos, y_pos), 'connections': [node_id1 ... node_idn]}}
• node_id_start - (int) node id of starting node in the map
• node_id_goal - (int) node id of terminating node in the map

Returns:

• list - containing node_ids that define shortest possible path between node_id_start and node_id_goal

Students were supplied with an extensive code template for this project. The student's (my) contributions occur after the "TODO" prompts. The student's (my) responsibilities included selecting and defining the data structures, and programming the algorithm's scoring components, including the heuristic cost estimator and the node selector.

• Dependencies and Pre-requisites
• Installation and Usage
• Known Issues
• Future Plans
• Support
• Contributing
• Authors and Acknowledgement
• License

• Python 3.x (including math and numbers libraries)

1. Copy files path_planner.py, helpers.py, and test.py to a directory,
2. In a terminal, navigate to the directory,
3. Execute python3 path_planner.py.

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Please open an issue to receive help.

Pull requests are welcome. Please open an issue to discuss any changes you would like to make or see.

This is coursework for the Udacity - Introduction to Self-Driving Cars Nano Degree

Unit: Navigating Data Structures Project: Implement Route Planner

MIT