Problem
Research questions and friction points this paper is trying to address.
underground mining
autonomous vehicles
optimization
motion planning
ore throughput
Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining
🤖 AI Summary
研究通过提出SAMM及变体SAMMS，解决地下矿井中自动车队的协调问题，优化了矿石运输计划，提高了效率和可扩展性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Block Cave Mining (BCM)
eventually optimal anytime solver
mixed-integer linear programming
task assignment and scheduling
path planning
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