Project: 3D Motion Planning

Required Steps for a Passing Submission:

Load the 2.5D map in the colliders.csv file describing the environment.
Discretize the environment into a grid or graph representation.
Define the start and goal locations.
Perform a search using A* or other search algorithm.
Use a collinearity test or ray tracing method (like Bresenham) to remove unnecessary waypoints.
Return waypoints in local ECEF coordinates (format for self.all_waypoints is [N, E, altitude, heading], where the drone’s start location corresponds to [0, 0, 0, 0].
Write it up.
Congratulations! Your Done!

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

Writeup / README

1. 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.

You're reading it! Below I describe how I addressed each rubric point and where in my code each point is handled.

Explain the Starter Code

1. Explain the functionality of what's provided in `motion_planning.py` and `planning_utils.py`

These scripts contain a basic planning implementation that including:

motion_planning
- MotionPlanning
  - Responsible for planning navigation (waypoints)
  - Implements state positions, transitions, and callbacks
- plan_path
  - Imports longitude and latitude for 2.5D map global positions from .csv
  - Identifies obstacles and creates a grid
  - Defines end goal position
  - Calculates ideal path using A*
  - Navigates to path while avoiding obstacles
planning_utils
- Source file for defining functions related to:
  - Finding the goal
  - Creating the grid
  - Defining locations
  - Validating actions
  - Creating a path for A*

...and here's a lovely image from Udacity!

Implementing Your Path Planning Algorithm

1. Set your global home position

Downtown San Francisco was designed in Unity as a realistic environment.

The csv file is has the initial latitude and longitude as the first entry. This data is then loaded into the program.

  with open('colliders.csv') as f:
      origin_pos_data = f.readline().split(',')
  lat0 = float(origin_pos_data[0].strip().split(' ')[1])
  lon0 = float(origin_pos_data[1].strip().split(' ')[1])

2. Set your current local position

Defining the local position relative to global home is an intuitive task. This can be completed similar to the following:

  # Set home position to (lon0, lat0, 0)
  self.set_home_position(lon0, lat0, 0)

  # Tetrieve current global position
  global_pos_current = [self._longitude, self._latitude, self._altitude]

  # Convert to current local position using global_to_local()
  local_pos_current = global_to_local(global_pos_current, self.global_home)

3. Set grid start position from local position

Simple enough, just initialize the grid with the given offsets.

  grid_start = (int(local_pos_current[0] -north_offset),
                int(local_pos_current[1] -east_offset))

4. Set grid goal position from geodetic coords

Latitude and longitude can be changed to any value (bounded, obviously) within the map and have it rendered to a goal location on the grid.

  global_goal = (-122.39827335, 37.79639627, 0)
  local_goal = global_to_local(global_goal, self.global_home)
  grid_goal = (int(local_goal[0] - north_offset),
               int(local_goal[1] - east_offset))

5. Modify A* to include diagonal motion (or replace A* altogether)

Now, some adjustments need to be done to the planning_utils.py file. More specicially, to implement a root2 function, we can add some additional coordinate parameters and check them:

  WEST = (0, -1, 1)
  EAST = (0, 1, 1)
  NORTH = (-1, 0, 1)
  SOUTH = (1, 0, 1)
  NE = (-1, 1, np.sqrt(2))
  NW = (-1, -1, np.sqrt(2))
  SE = (1, 1, np.sqrt(2))
  SW = (1, -1, np.sqrt(2))

  ................................................................................

  if x - 1 < 0 or grid[x - 1, y] == 1:
      valid_actions.remove(Action.NORTH)
  if x + 1 > n or grid[x + 1, y] == 1:
      valid_actions.remove(Action.SOUTH)
  if y - 1 < 0 or grid[x, y - 1] == 1:
      valid_actions.remove(Action.WEST)
  if y + 1 > m or grid[x, y + 1] == 1:
      valid_actions.remove(Action.EAST)

  if x - 1 < 0 or y + 1 < 0 or grid[x - 1, y + 1] == 1:
      valid_actions.remove(Action.NE)
  if x - 1 < 0 or y - 1 < 0 or grid[x - 1, y - 1] == 1:
      valid_actions.remove(Action.NW)
  if x + 1 < 0 or y + 1 < 0 or grid[x + 1, y + 1] == 1:
      valid_actions.remove(Action.SE)
  if x + 1 < 0 or y - 1 < 0 or grid[x + 1, y - 1] == 1:
      valid_actions.remove(Action.SW)

Now, the default A* feature can be implemented.

  if current_node == goal:
      print('Found a path.')
      found = True
      break
  else:
      for action in valid_actions(grid, current_node):
          # get the tuple representation
          da = action.delta
          next_node = (current_node[0] + da[0], current_node[1] + da[1])
          branch_cost = current_cost + action.cost
          queue_cost = branch_cost + h(next_node, goal)

          if next_node not in visited:
              visited.add(next_node)
              branch[next_node] = (branch_cost, current_node, action)
              queue.put((queue_cost, next_node))
................................................................................

          print('Local Start and Goal: ', grid_start, grid_goal)
          path, _ = a_star(grid, heuristic, grid_start, grid_goal)

...but why stop there when using Blum's Medial Axis version of A* can be used with the topological skeletons! (Remember to use the skimage.morphology library)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3663081/

  # A* == Medial
    topological_skeleton = medial_axis(invert(grid))
    adjacent_grid_start, adjacent_grid_goal =
        find_goal(topological_skeleton, grid_start, grid_goal)

Hey! It looks like we got something!

6. Cull waypoints

It was recommended to use a collinearity test or ray tracing (Bresenham's method) to prune superfluous waypoints. This is achieved by characterizing points based on the line-of-sight of the path to the goal and eliminating unnecessary waypoints. This increases the efficiency and performance of the system, as the drone has a protocol to follow all compulsory waypoints precisely, which can be problematic when the simple program causes the drone to "overshoot" its trajectory (which causes oscillations when reorienting).

Thus, a path pruning feature was defined in the planning_utils:

  def path_pruning(path):
      prune_path = [p for p in path]

      i = 0
      prune_count = 0
      while i < len(prune_path) - 2:
          p1 = point(prune_path[i])
          p2 = point(prune_path[i+1])
          p3 = point(prune_path[i+2])

          # Remove superfluous points
          if collinearity_check(p1, p2, p3):
              prune_path.remove(prune_path[i+1])
              prune_count += 1
          else:
              i += 1
      print('Pruning Path', prune_count)
      return prune_path

Execute the flight

1. Does it work?

It works!

Double check that you've met specifications for each of the rubric points.

Extra Challenges: Real World Planning

For an extra challenge, consider implementing some of the techniques described in the "Real World Planning" lesson. You could try implementing a vehicle model to take dynamic constraints into account, or implement a replanning method to invoke if you get off course or encounter unexpected obstacles.

Put all of these together and make up your own crazy paths to fly! Can you fly a double helix??

Okay, flying a double helix might seem like a silly idea, but imagine you are an autonomous first responder vehicle. You need to first fly to a particular building or location, then fly a reconnaissance pattern to survey the scene! Give it a try!

Ohara124c41/FCND-Drone-3D_Motion_Planning