Scalable planning coordinates autonomous vehicles in underground mining
Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining
Robotics
Summary
Coordinating many autonomous vehicles in underground mines is hard because the environment changes and lots of planning is needed. The authors define a new problem called Block Cave Mining that focuses on moving ore efficiently while following mining rules. They created SAMM, a solver that plans tasks and paths together to eventually find the best solutions, and SAMMS, a faster version that works well on bigger problems with near-best results. Their tests show these methods can handle realistic mining setups and large vehicle fleets.
What this means in practice
- •For mining operations managers: Optimize vehicle coordination in underground mines to improve ore extraction efficiency while respecting operational constraints.
- •For industrial automation engineers: Design control software for autonomous mining fleets that balances task scheduling and collision-free routing in complex, dynamic environments.
Authors
Christopher Leet, Paolo Forte, Uwe Köckemann, Henrik Andreasson, Sven Koenig
Abstract
Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coordinating fleets of autonomous vehicles in dynamic mine environments introduces substantial challenges in both optimization and motion planning. To address these challenges, we introduce and formalize the \emph{Block Cave Mining (BCM)} problem, which focuses on computing a transport plan that maximizes ore throughput while satisfying draw ratio constraints. To solve this problem, we propose SAMM, an eventually optimal anytime solver that jointly integrates task assignment, scheduling, and path planning via a mixed-integer linear programming formulation. To improve scalability, we also introduce SAMMS, a variant of SAMM that trades optimality guarantees for efficiency by decomposing the problem into shorter planning subcycles. Experimental evaluations using realistic industrial mine scenarios demonstrate that SAMMS achieves near-optimal throughput and scales effectively to larger fleets and mine layouts.