Path Planner for Autonomous Exploration of Underground Mines by Aerial Vehicles.

Area of Science:

• Robotics
• Autonomous Systems
• Geological Engineering

Background:

• Underground mines lack navigation signals and communication, posing challenges for robotic exploration.
• Autonomous aerial robots (multicopters) require robust path planning for confined and complex environments.

Purpose of the Study:

• To develop a computationally efficient path planner for autonomous exploration of single-level underground mines using aerial robots.
• To enable navigation in environments with limited sensing and communication capabilities.

Main Methods:

• A novel path planner algorithm based on exploration vectors and a reinforced open sector method.
• Integration of a Laser Imaging Detection and Ranging (LIDAR) sensor with Simultaneous Localization and Mapping (SLAM) capability.
• Implementation of global awareness and obstacle avoidance modules for loop prevention and narrow passage navigation.

Main Results:

• The path planner demonstrated optimal routing and high path efficiency in Hardware-in-the-loop (HIL) simulations.
• The algorithm proved to be fast, real-time, and memory-efficient.
• Successful autonomous exploration of simulated single-level mining tunnels was achieved.

Conclusions:

• The proposed path planner is a feasible solution for autonomous underground mine exploration with aerial robots.
• The algorithm's efficiency and robustness make it suitable for real-world applications in GPS-denied environments.
• This work advances the capabilities of autonomous systems in hazardous and unmapped subterranean settings.