3D Motion Planning

Provide a Writeup / README that includes all the rubric points and how you addressed each one. You can submit your writeup as markdown or pdf.

The writeup / README should include a statement and supporting figures / images that explain how each rubric item was addressed, and specifically where in the code each step was handled.

Test that motion_planning.py is a modified version of backyard_flyer_solution.py for simple path planning. Verify that both scripts work. Then, compare them side by side and describe in words how each of the modifications implemented in motion_planning.py is functioning.

The goal here is to understand the starter code. We've provided you with a functional yet super basic path planning implementation and in this step, your task is to explain how it works! Have a look at the code, particularly in the plan_path() method and functions provided in planning_utils.py and describe what's going on there. This need not be a lengthy essay, just a concise description of the functionality of the starter code.

In the starter code, we assume that the home position is where the drone first initializes, but in reality you need to be able to start planning from anywhere. Modify your code to read the global home location from the first line of the colliders.csv file and set that position as global home (self.set_home_position())

Here you should read the first line of the csv file, extract lat0 and lon0 as floating point values and use the self.set_home_position() method to set global home.

In the starter code, we assume the drone takes off from map center, but you'll need to be able to takeoff from anywhere. Retrieve your current position in geodetic coordinates from self._latitude, self._longitude and self._altitude. Then use the utility function global_to_local() to convert to local position (using self.global_home as well, which you just set)

Here as long as you successfully determine your local position relative to global home you'll be all set.

In the starter code, the start point for planning is hardcoded as map center. Change this to be your current local position.

This is another step in adding flexibility to the start location. As long as it works you're good to go!

In the starter code, the goal position is hardcoded as some location 10 m north and 10 m east of map center. Modify this to be set as some arbitrary position on the grid given any geodetic coordinates (latitude, longitude)

This step is to add flexibility to the desired goal location. Should be able to choose any (lat, lon) within the map and have it rendered to a goal location on the grid.

Write your search algorithm. Minimum requirement here is to add diagonal motions to the A* implementation provided, and assign them a cost of sqrt(2). However, you're encouraged to get creative and try other methods from the lessons and beyond!

Minimal requirement here is to modify the code in planning_utils() to update the A* implementation to include diagonal motions on the grid that have a cost of sqrt(2), but more creative solutions are welcome. In your writeup, explain the code you used to accomplish this step.

Cull waypoints from the path you determine using search.

For this step you can use a collinearity test or ray tracing method like Bresenham. The idea is simply to prune your path of unnecessary waypoints. In your writeup, explain the code you used to accomplish this step.

This is simply a check on whether it all worked. Send the waypoints and the autopilot should fly you from start to goal!

At the moment there is some mismatch between the colliders map and actual buildings in the scene. To ensure success build in a 5+ m safety margin around obstacles. Try some different goal locations. Also try starting from a different point in the city. Your reviewer will also try some random locations so be sure to test your solution! There is no firm constraint or requirement on how accurately you land exactly on the goal location. Just so long as your planner functions as expected.