解决地下矿山多车协同运输的高效规划问题，提升矿石运输效率。
Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining
- 采用混合整数线性规划联合优化任务分配、调度与路径规划
- 在真实矿山场景中实现近似最优矿石吞吐量，支持大规模车队
- 适合需要高效率、可扩展性的智能矿山系统设计者参考
地下矿山自动化有望显著提升安全、运营效率和可持续性。然而，在动态矿场环境中协调大量自主车辆，在优化与运动规划方面仍面临巨大挑战。为此，我们提出并形式化了块状崩落采矿（Block Cave Mining, BCM）问题，旨在计算最大化矿石吞吐量且满足放矿比率约束的运输方案。我们提出SAMM，一种最终最优的即时求解器，通过混合整数线性规划联合处理任务分配、调度与路径规划。为提升可扩展性，还引入SAMMS，其通过将问题分解为较短的规划子周期，以牺牲部分最优性保障换取更高效率。在真实工业矿场场景下的实验表明，SAMMS实现了接近最优的吞吐量，并能有效扩展至更大规模车队与矿场布局。
原文摘要 · Abstract (English)
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.
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