Steps to complete the project:

1. Download the simulator appropriate for your OS (MacOS, Linux, Windows)
2. Get setup with Python using the Python Starter Kit
3. Fork, download or clone the project repository and have a look at the README.
4. Experiment with the simulator and take some data ( explained in the Telemetry and Record Data lesson)
5. Run through the Jupyter notebook and fill in the process_image() function.
6. Run drive_rover.py and experiment with autonomous mapping (details in the More Decisions lesson).
7. Fill in the perception_step() and decision_step() functions and map the environment!

Note: running the simulator with different choices of resolution and graphics quality may produce different results, particularly on different machines! Make a note of your simulator settings (resolution and graphics quality set on launch) and frames per second (FPS output to terminal by drive_rover.py) in your writeup when you submit the project so your reviewer can reproduce your results.

Project Summary

In this project, you will be writing code to autonomously map a simulated environment and search for samples of interest. To complete this project you will use the tools you learned about in the lesson, and build upon them.

Your first goal is to get the simulator up and running. Once you can successfully run the simulator in training mode, you can record some data and test out the functionality of the code in the Jupyter notebook.

Next, you'll add code to the perception_step() and decision_step() functions (inside of perception.py and decision.py respectively) to get your rover to navigate and map autonomously!

Finally, you'll make a brief writeup report. The github repository has a writeup_template.md that can be used as a guide.

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 Project Demo Video. of the basic implementation for you.

Evaluation

Once you have completed your project, use the Project Rubric to review the project. If you have covered all of the points in the rubric, then you are ready to submit! If you see room for improvement in any category in which you do not meet specifications, keep working!

Your project will be evaluated by a Udacity reviewer according to the same Project Rubric. Your project must "meet specifications" in each category in order for your submission to pass.

Submission

What to include in your submission

You may submit your project as a zip file or with a link to a GitHub repo. The submission must include these items:

• Jupyter Notebook with your test code
• Test output video
• Autonomous navigation scripts:
  • drive_rover.py
  • supporting_functions.py
  • decision.py
  • perception.py
• writeup report (md or pdf file)

Ready to submit your project?

Click on the "Submit Project" button and follow the instructions to submit! Please fill out the Project Feedback Form after you have completed the project.