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Autonomous rover operation and obstacle avoidance code for a simulated rover in a gazebo simulation.

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Rover Autonomous Operation Algorithm

Project Overview

This repository hosts the Rover Autonomous Operation Algorithm, a sophisticated project undertaken for APSC 103 at Queen's University. The algorithm is part of a broader initiative by the Queen's Space Engineering Team (QSET) targeting enhancements in autonomous navigation for a Mars rover prototype. It leverages a combination of sensors, including cameras and LiDAR, to navigate challenging terrains autonomously. This software is designed for integration with the Robot Operating System (ROS) and supports deployment on simulated platforms as well as potential real-world applications.

Code Documentation

The algorithm is thoroughly documented, detailing the operational framework of each module within ROS for autonomous navigation and obstacle detection.

Compatibility

This software is compatible with Python version 2.7.17. Ensure your development environment is set up with this Python version to avoid any compatibility issues.

Proprietary Library: QSET Lib

The Rover Autonomous Operation Algorithm utilizes a proprietary library, QSET lib, which is essential for its functionality. The QSET Lib can be accessed and downloaded from the following GitLab repository: https://gitlab.com/qset-mod-3/qset-lib. Please ensure to include this library in your project setup for proper operation of the algorithm.

Gazebo Simulation

The necessary files for running the Rover simulation in Gazebo can be found at the following GitLab repository: https://gitlab.com/qset-mod-3/unified-launch. These files are crucial for simulating the rover's autonomous operations in a virtual environment.

License

The project is available under the MIT License. For full license details, refer to the LICENSE.md file.

Acknowledgments

  • Thanks to Charlotte Reed, ECE Manager, for her invaluable guidance.
  • Gratitude to the APSC 103 student groups for their contributions towards the navigation and object detection algorithms.
  • Special thanks to Connor Rewa, Project Manager for APSC 103, for his leadership and management throughout the project.
  • Appreciation to Dr. Majid Pahlevani, Faculty Advisor, for his expert advice and continuous support.

Authors

Will Palacek—All coding and project lead. Responsible for developing the Rover Autonomous Operation Algorithm.

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Autonomous rover operation and obstacle avoidance code for a simulated rover in a gazebo simulation.

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