构建首个开源地下矿井强化学习探索基准，支持真实矿井环境模拟与高效训练。
MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
- 基于真实矿井数据重建10万平米隧道网络，融合激光扫描地形与物理材质分区。
- 在五组随机种子下，策略覆盖率达88.89%，3组达到90%目标，验证可复现性。
- 适合研究无卫星定位环境下自主导航的机器人学者与工业应用开发者。
地下矿井对自主机器人导航构成极端挑战：无GPS信号、光照差、巷道拓扑环状且非凸。现有开源仿真基准缺乏基于真实生产矿井几何结构且兼容GPU加速学习流程的方案。本文提出MineXplore，一个基于MuJoCo的开源导航基准，源自Leung等（2017）智利铜矿的真实数据集。环境通过六阶段轮廓转MJCF流程重建了104,423平方米的隧道网络，包含八边形断面、激光雷达获取的锯齿状墙壁、三种地形摩擦区、全局5度倾角及周期性点光源。几何保真度经交并比（IoU）验证达0.9538，表面纹理相似度在六个结构维度上平均为79.4%。使用RLlib训练的单智能体PPO基线在五组独立随机种子下取得最佳滚动覆盖率88.89%（其中三组达到90%覆盖目标），证实MineXplore可在真实感知与拓扑条件下支持稳定、可复现的策略学习。
原文摘要 · Abstract (English)
Underground mines present extreme conditions for autonomous robot navigation: GPS is denied, lighting is degraded, and tunnel topology is loop-rich and non-convex. Simulation benchmarks grounded in real production-mine geometry and compatible with GPU-accelerated learning pipelines do not yet exist in the open-source ecosystem. We present MineXplore, an open-source MuJoCo-based navigation benchmark derived from the Leung et al. 2017 Chilean underground copper mine dataset. The environment reconstructs a 104,423 sq.m tunnel network through an six-stage contour-to-MJCF pipeline incorporating octagonal wall cross-sections, LiDAR-sourced jagged wall geometry, three terrain friction zones, a global 5 degree incline, and periodic spot lighting. Geometric fidelity is validated at an Intersection over Union (IoU) of 0.9538 against the source survey map, and surface texture similarity scores 79.4% across six structural dimensions. A single-agent PPO baseline trained via RLlib across five independent random seeds achieves a best rolling coverage of 88.89% (3 of 5 seeds reaching the 90% coverage target), confirming that MineXplore supports stable and reproducible policy learning under realistic underground sensing and topology.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。