发表机构
University of Southern California; Centre for Applied Autonomous Sensor Systems, Örebro University（南加州大学; 厄勒布鲁大学应用自主传感器系统中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对地下矿山自动化中的车队协调难题，提出BCM问题及SAMM/SAMMS求解器，通过混合整数线性规划联合优化任务分配、调度与路径规划，在真实场景中实现近最优吞吐量和良好可扩展性。
AI 中文摘要
地下采矿自动化有潜力显著提升安全性、运营效率和可持续性。然而，在动态矿山环境中有效协调自主车辆车队，在优化和运动规划方面都带来了巨大挑战。为应对这些挑战，我们引入并形式化了块体崩落开采（BCM）问题，该问题专注于计算一个运输计划，在满足放矿比率约束的同时最大化矿石吞吐量。为解决该问题，我们提出了SAMM，一种最终最优的随时求解器，通过混合整数线性规划公式联合集成任务分配、调度和路径规划。为提高可扩展性，我们还引入了SAMMS，这是SAMM的一种变体，通过将问题分解为较短的规划子周期，以最优性保证换取效率。使用真实工业矿山场景的实验评估表明，SAMMS实现了接近最优的吞吐量，并能有效扩展到更大的车队和矿山布局。
英文摘要
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.
