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Search and Sample Return Project
This project is modeled after the NASA sample return challenge and it will give you first hand experience with the three essential elements of robotics, which are perception, decision making and actuation. You will carry out this project in a simulator environment built with the Unity game engine.
You can test out the simulator by opening it up and choosing "Training Mode". Use the mouse or keyboard to navigate around the environment and see how it looks.
You'll need Python 3 and Jupyter Notebooks installed to do this project. The best way to get setup with these if you are not already is to use Anaconda following along with the RoboND-Python-Starterkit.
Here is a great link for learning more about Anaconda and Jupyter Notebooks
I've saved some test data for you in the folder called
test_dataset. In that folder you'll find a csv file with the output data for steering, throttle position etc. and the pathnames to the images recorded in each run. I've also saved a few images in the folder called
calibration_images to do some of the initial calibration steps with.
The first step of this project is to record data on your own. To do this, you should first create a new folder to store the image data in. Then launch the simulator and choose "Training Mode" then hit "r". Navigate to the directory you want to store data in, select it, and then drive around collecting data. Hit "r" again to stop data collection.
Included in the IPython notebook called
Rover_Project_Test_Notebook.ipynb are the functions from the lesson for performing the various steps of this project. The notebook should function as is without need for modification at this point. To see what's in the notebook and execute the code there, start the jupyter notebook server at the command line like this:
This command will bring up a browser window in the current directory where you can navigate to wherever
Rover_Project_Test_Notebook.ipynb is and select it. Run the cells in the notebook from top to bottom to see the various data analysis steps.
The last two cells in the notebook are for running the analysis on a folder of test images to create a map of the simulator environment and write the output to a video. These cells should run as-is and save a video called
test_mapping.mp4 to the
output folder. This should give you an idea of how to go about modifying the
process_image() function to perform mapping on your data.
The file called
drive_rover.py is what you will use to navigate the environment in autonomous mode. This script calls functions from within
decision.py. The functions defined in the IPython notebook are all included in
perception.py and it's your job to fill in the function called
perception_step() with the appropriate processing steps and update the rover map.
decision.py includes another function called
decision_step(), which includes an example of a conditional statement you could use to navigate autonomously. Here you should implement other conditionals to make driving decisions based on the rover's state and the results of the
drive_rover.py should work as is if you have all the required Python packages installed. Call it at the command line like this:
Then launch the simulator and choose "Autonomous Mode". The rover should drive itself now! It doesn't drive that well yet, but it's your job to make it better!
Note: running the simulator with different choices of resolution and graphics quality may produce different results! Make a note of your simulator settings in your writeup when you submit the project.
If you're struggling to get started on this project, or just want some help getting your code up to the minimum standards for a passing submission, we've recorded a walkthrough of the basic implementation for you but spoiler alert: this Project Walkthrough Video contains a basic solution to the project!.