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Search and Sample Return Project writeup

Describe in your writeup (and identify where in your code) how you modified or added functions to add obstacle and rock sample identification.

Obstacle identification is implemented with a color thresholding in RGB color space. With the pixel values in the range (0, 90) - R, (0,255) - G, (0, 140) - B. Exact values was tuned through the interactive widgets feature in Jupyter notebook.

The relevant code in perception.py:191-197

thresh_wall = ((0,90), (0, 255), (0, 140)) # RGB

obs_color_select = np.zeros_like(image[:,:,0])
obs_thresh = (image[:,:,0] > thresh_wall[0][0]) & (image[:,:,0] < thresh_wall[0][1]) \
            & (image[:,:,1] > thresh_wall[1][0]) & (image[:,:,1] < thresh_wall[1][1]) \
            & (image[:,:,2] > thresh_wall[2][0]) & (image[:,:,2] < thresh_wall[2][1])
obs_color_select[obs_thresh] = 1
obs_warped = perspect_transform(obs_color_select, source, destination)
obs_masked = np.multiply(obs_warped, Rover.view_mask)

For rock sample identification I've made a conversion to the HSV color space and tuned the relevant parameters for H - (0,30), S - (130, 255), V - (130, 255)

thresh_diamond = ((0,30), (130,255), (130,255)) # HSV

diam_clipped = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
diam_color_select = np.zeros_like(diam_clipped[:,:,0])
diam_thresh = (diam_clipped[:,:,0] > thresh_diamond[0][0]) & (diam_clipped[:,:,0] < thresh_diamond[0][1]) \
            & (diam_clipped[:,:,1] > thresh_diamond[1][0]) & (diam_clipped[:,:,1] < thresh_diamond[1][1]) \
            & (diam_clipped[:,:,2] > thresh_diamond[2][0]) & (diam_clipped[:,:,2] < thresh_diamond[2][1])
diam_color_select[diam_thresh] = 1
diam_warped = perspect_transform(diam_color_select, source, destination)

For obstacle and navigable pixel identification I've also applied the view_mask that selects only pixels in the range of y = [85, 160] for obstacle and y = [85, 110] for navigable pixels. Mask is obtained in get_view_mask():

def get_view_mask(image_shape = (160, 320, 3), horizon=75, bottom=0):
    white_image = np.ones(image_shape, dtype=np.uint8)*255

    source, destination = get_src_dst()

    white_image = clip_to_view_horizon(white_image, horizon, bottom)

    warped_white = perspect_transform(white_image, source, destination)

    mask = color_thresh(warped_white, (250, 250, 250))

    return mask

Describe in your writeup how you modified the process_image() to demonstrate your analysis and how you created a worldmap. Include your video output with your submission.

process_image() in general follows what was suggested in the comments. Find thresholded pixels for navigable area, obstacles and rocks separately and combine them in one worldmap image in different RGB channels. So Red is obstacles, Blue - navigable area and Green - rocks. Before we can add pixels to the map we also should transform them to bird-eye view and rotate/translate/scale to the worldmap coordinates.

perception_step() and decision_step() functions have been filled in and their functionality explained in the writeup.

In the perception_step() we are identifying navigable areas, obstacles and rocks and map them to the worldmap. Only pixels that was taken with roll and pitch below 2 degree considered as a good pixels because otherwise our bird-eye transformation will have a big error due to constant src and destination points. Ideally we should factor it and automatically warp picture for all possible angles of roll and pitch or we lose a lot of information from our vision.

Also nav_angles and rock_angles is storing for later use in decision_step() and control_step()

I've added additional method that removes rock from the map when rover finished picking it up, so rock disappears from the map when it picked up.

decision_step() default mode is follow_wall when rover stick to the left wall. Other modes is rotate_right used when there is not enough navigable pixels or rover is stuck in sand or rock or wall inclination. forward_stop mode is using to start rock hunting when there is visible rock pixels.

Additionally I've introduced control_step where a simple PD controller is implemented.

There also was a lot of experiments in PD controller for throttle and it's combination with PD controller for steering that didn't worked well for stopping. Also I've thought initially that I can build planning and navigation mostly on map, but find a lot of problem with map inaccuracy for such planning and combination of map data with actual situational vision data. So I rolled back everything to the simplest wall following solution that works reasonable well.

Hope in following parts of the course we will have more chance to build a proper controller, planner and decision step that works much better together.

By running drive_rover.py and launching the simulator in autonomous mode, your rover does a reasonably good job at mapping the environment.

The result of running drive_rover.py in video below. 13-15 FPS during video recording, and around 22 FPS without. Simulator mode - 640x480, Fastest.

Rover Driving and Mapping

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