Search and Sample Return Project

Commands

conda info --envs
conda create -n RoboND python=3.5
source activate RoboND
jupyter notebook
python drive_rover.py

The goals / steps of this project are the following:

• Goal is to get Rover driving autonomously and find and pickup rocks
• Set up mini-conda environment for python code
• Complete all Training in Jupyter Notebook
• Record Video
• Install Unity Engine Simulator
• Drive Rover with python via Unity Engine
• Map at least 40% of the environment with 60% fidelity
• Find at least 1 rock

Training / Calibration

• Download the simulator and take data in "Training Mode"
• Test out the functions in the Jupyter Notebook provided
• Add functions to detect obstacles and samples of interest (golden rocks)
• Fill in the process_image() function with the appropriate image processing steps (perspective transform, color threshold etc.) to get from raw images to a map. The output_image you create in this step should demonstrate that your mapping pipeline works.
• Use moviepy to process the images in your saved dataset with the process_image() function. Include the video you produce as part of your submission.

Autonomous Navigation / Mapping

• Fill in the perception_step() function within the perception.py script with the appropriate image processing functions to create a map and update Rover() data (similar to what you did with process_image() in the notebook).
• Fill in the decision_step() function within the decision.py script with conditional statements that take into consideration the outputs of the perception_step() in deciding how to issue throttle, brake and steering commands.
• Iterate on your perception and decision function until your rover does a reasonable (need to define metric) job of navigating and mapping.

Rubric Points

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

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Writeup / README

Notebook Analysis

1. Run the functions provided in the notebook on test images (first with the test data provided, next on data you have recorded). Add/modify functions to allow for color selection of obstacles and rock samples.

1. Populate the process_image() function with the appropriate analysis steps to map pixels identifying navigable terrain, obstacles and rock samples into a worldmap. Run process_image() on your test data using the moviepy functions provided to create video output of your result.

And another!

Autonomous Navigation and Mapping

1. Fill in the perception_step() (at the bottom of the perception.py script) and decision_step() (in decision.py) functions in the autonomous mapping scripts and an explanation is provided in the writeup of how and why these functions were modified as they were.

perception_step() function

1. Defined source and destination points for perspective transform
2. Applied perspective transform and mask on obstacles
3. Applied color threshhold using range of numbers and created obstacle map from the masked perspective transform
4. Updated image on left side of screen to see what is range of view
5. Convert map image to rover centric coordinates
6. Convert rover centric values to world coordinates
7. Updated map image on right side of screen
8. Convert rover-centric pixel positions to polar coordinates, update angles and distances

2. Autonomous Navigation with Unity

  Screen Resolution: 1024x768

  Graphics Quality: Good

  FPS: 15

  Rover is a basic wall follower with a bias of 13 and a side to side range of motion of -10/10. I gave the thresh a range so I could use the color thresh function for rocks too. Used a mask for the obstacles.

Issues

  Rover still gets stuck in loop and on obstacles sometimes. It picks up rocks if they are right in front of it. Would like to figure out way to eliminate area that it's already travelled. Stops following left wall if the curve is too large or if the Rover is pointing the wrong way when it goes around some turns.