MineXplore: 面向GNSS拒止地下环境的开源强化学习探索基准
MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
AI总结：
提出基于真实矿井数据的开源MuJoCo导航基准MineXplore，通过六阶段管道重建隧道网络，验证了在GNSS拒止、光照退化等极端条件下策略学习的稳定性与可复现性。
摘要：
地下矿井为自主机器人导航带来了极端条件：GPS被拒止，光照退化，隧道拓扑具有丰富的环路且非凸。目前开源生态中尚不存在基于真实生产矿井几何结构且兼容GPU加速学习管道的仿真基准。我们提出了MineXplore，一个基于Leung等人2017年智利地下铜矿数据集的开源MuJoCo导航基准。该环境通过六阶段轮廓到MJCF管道重建了一个104,423平方米的隧道网络，包含八边形墙壁横截面、LiDAR源锯齿状墙壁几何、三个地形摩擦区域、全局5度倾斜和周期性点光源。几何保真度通过交并比（IoU）为0.9538（与源测量图对比）得到验证，表面纹理相似度在六个结构维度上达到79.4%。通过RLlib在五个独立随机种子上训练的单智能体PPO基线实现了88.89%的最佳滚动覆盖率（5个种子中有3个达到90%覆盖目标），证实MineXplore在真实地下感知和拓扑条件下支持稳定且可复现的策略学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
