State-of-the-Art Path Optimisation for Automated Open-Pit Mining Drill Rigs: A Deterministic Approach

This study introduces a deterministic framework for optimising the path planning of autonomous drill rigs in open-pit mining operations. While prior research has primarily focused on automating drilling mechanics, this study addresses the essential but underexplored phase of tramming, defined as the rig’s non-productive movement between holes. The proposed approach integrates geometric pattern recognition and slope-based route alignment. It also incorporates practical maneuverability constraints to generate efficient, smooth, and safe paths. Unlike evolutionary algorithms, which suffer from variability and demand extensive computation, this method delivers fast and consistent results. These are well-suited to the dynamic conditions of real-world mining. Applied to a 1596-hole case study, the framework reduced tramming time by over 50%, shortening the total project duration by 8% compared with the actual project. The findings demonstrate its potential to improve both operational efficiency and commercial readiness for autonomous drilling systems.