ABSTRACT The automation and intelligence of underground mining vehicles are vital for ensuring safety and improving production efficiency, representing an essential trend in the evolution of the mining industry. However, achieving autonomous navigation for load‐haul‐dump (LHD) vehicles in GPS‐denied underground environments poses significant challenges. To address these challenges, we introduce a novel hybrid navigation (HN) strategy that combines the strengths of absolute navigation (AN), which relies on precise localization using pre‐mapped environments, with reactive navigation (RN), which utilizes real‐time sensor data for immediate navigation decisions. In this strategy, the AN facilitates map‐referenced positioning during turns, while the RN dynamically adjusts the trajectory on straight segments through real‐time sensor feedback, independent of absolute localization. This integration enhances the robustness of navigation. We conducted simulation experiments to compare RN, AN, and HN systems. The results demonstrate that the HN system effectively merges the adaptability of RN with the precision of AN, ensuring reliable navigation through narrow intersections and stable performance on straight paths. Field trials further validated the HN system's ability to operate an LHD vehicle at a linear speed of approximately 1.8 m/s and a turning speed of 0.6 m/s, underscoring its practical applications in real‐world scenarios. These findings highlight the HN system's potential for robust autonomous operation in complex underground environments.
Jiang et al. (Mon,) studied this question.