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Interactive Robotics Laboratory
Yu Gu, Professor


Stonemine - Autonomous robots exploring underground for mine safety.

We developed Rhino, an autonomous ground vehicle that can navigate underground mine environments and generate 3D maps. The project aims to build a platform capable of performing autonomous inspection of underground mines, by identifying possible hazards and structural integrity of the mine, protecting miners from possible accidents.

This project is also part of a collaboration with NAVLAB and FARO lab that includes collaborative inspection with a tethered drone. Rhino is designed to carry the tethered drone, its winch system, and batteries as a payload, to extend the aerial vehicle’s operational time and to inspect difficult-to-reach areas.

The system being developed is a skid-steer, four-wheeled, split-body, unmanned ground vehicle (UGV) that uses a LiDAR and IMU to perform long-term autonomous navigation. Maps are generated through a LIO-SAM framework. The system has been tested in different environments and terrains to ensure its robustness and ability to operate for extended periods of time while also generating 3D maps. Project aims to develop exploration algorithms capable of autonomously exploring and mapping unknown regions without communication with human operators.


  1. Robotic Platform Development: Design and build a rugged robotic platform capable of navigating complex and hazardous underground mine environments for long periods of time and carrying the tethered drone payload. The robot should be equipped with a variety of sensors for terrain mapping, obstacle avoidance, and data collection.
  2. Autonomous Navigation: Develop advanced navigation algorithms that allow the robot to autonomously traverse the mine while avoiding obstacles, handling uneven terrain, and adapting to changing environmental conditions. The robot should be capable of mapping its path and surroundings in 3D.
  3. Data Collection and Mapping: Implement software for the robot to systematically collect data during its exploration. This data will include point cloud data from LiDAR, images from cameras, that should be used for analyzing the mine safety conditions.
  4. Autonomous Exploration: Deployment of robotic systems that are capable of navigating and exploring the underground mine environments without direct human intervention, and with decision-making capabilities that enables the robot to operate in the harsh underground mine and adapt to unexpected situations.
  5. Validation and Testing: Conduct extensive testing of the robotic platform inside underground mine environments. Refine the system based on feedback and real-world performance.


  • Improved Safety: Reduce the risk to human miners by deploying robots in hazardous environments.
  • Efficient Resource Management: Use accurate mine maps and real-time data to optimize resource allocation and mine planning.
  • Environmental Monitoring: Analysis of structural integrity to prevent accidents.
  • Data-Driven Decision Making: Enable mine operators to make informed decisions based on real-time data and visualizations.


Tatsch, Christopher, et al. "Rhino: An Autonomous Robot for Mapping Underground Mine Environments." 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2023.  View on ArXiv