Abstract:
Autonomous navigation in underground mining poses a unique set of challenges, from GPS
unavailability and high dust levels that compromise visual sensors to feature-poor environments
that complicate localization and narrow tunnels that restrict vehicle movement. To address
these issues, this paper presents a novel behavior-based control approach, integrating wall-following for lateral stability as the vehicle progresses toward designated positions. The path to
these targets is generated by an A* algorithm, ensuring efficient route planning within confined
spaces. For localization, an Extended Kalman Filter (EKF) fuses data from wheel odometry
and an Inertial Measurement Unit (IMU), providing robust state estimation in the absence
of GPS. The proposed system leverages a four-wheel steering mechanism with negative-phase
control and is equipped with 3D LiDAR, ultrasonic sensors, wheel encoders, and an IMU for
enhanced situational awareness and control. Simulation results validate the system’s ability to
achieve precise navigation in challenging underground environments, even within tunnels that
allow minimal clearance.
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