Mars Rover Project: NASA Search and Sample Return Challenge!

This search and sample return project was based after the NASA sample return challenge, and it provides experience with the three essential elements of robotics, which are perception, decision making and actuation. This specific project uses the Unity game engine to simulate the environment.

Dependencies

The included code requires Python 3 and many dependencies. Using Anaconda is the easiest way to get things working. clone the repositry and follow these steps

• conda create --name <ENVIOREMENT NAME> --file requirements.txt
• conda activate <ENVIOREMENT NAME>

Or

• conda create -n <ENVIOREMENT NAME>
• conda activate <ENVIOREMENT NAME>
• conda install --file requirements.txt

Run the Code

You can test out the simulator by opening it up and choosing "Training Mode."

To run the automated code included in this repository:

• Activate the conda environment with conda activate <ENVIOREMENT NAME> (setup by following the instructions here)
• Run python ./code/drive_rover.py to start the automation logic (this communicates with the simulator directly)
• Start the simulator (double click Roversim.x86_64 or Roversim.x86) and choose "Autonomous Mode."

To Run ROS

• roscore
• rosrun gmapping slam_gmapping scan:=base_scan _particles:=30 _temporalUpdate:=0.01 _map_update_inerval:=1.0 _resampleThreshold:=4
• rviz

Press Ctrl+O to and open GMapping_Config.rvoz

• conda activate <ENVIOREMENT NAME>
• python ./code/drive_rover.py

Notebook Analysis

This Jupyter Notebook includes all of the major functions, which are broken out into individual sections as follows: