扩展机器人自主探索系统，实现更大地下环境的高效导航与协作。
An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
- 改进算法实现大范围几何与语义地图构建
- 支持大规模环境下路径规划与任务调度
- 适用于复杂地下场景的多模态协同探索
本文是团队在达伯拉地下挑战赛（DARPA Subterranean Challenge）中提出的自主系统NeBula的补充。针对更大规模地下环境的探索需求，论文在硬件、软件及算法层面进行了扩展：包括大尺度几何与语义环境建图、自适应定位系统、概率性可通行性分析与局部规划、基于部分可观马尔可夫决策过程（POMDP）的大规模全局运动规划与探索行为、大规模网络化与分布式推理、通信感知的任务规划，以及地面-空中多模态协同探索方案。文中展示了这些系统在石灰岩矿洞等大型地下环境中的实际应用与部署效果，验证了其在真实复杂场景下的可行性与鲁棒性。
原文摘要 · Abstract (English)
This paper presents an appendix to the original NeBula autonomy solution developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), participating in the DARPA Subterranean Challenge. Specifically, this paper presents extensions to NeBula's hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: (i) large-scale geometric and semantic environment mapping; (ii) an adaptive positioning system; (iii) probabilistic traversability analysis and local planning; (iv) large-scale POMDP-based global motion planning and exploration behavior; (v) large-scale networking and decentralized reasoning; (vi) communication-aware mission planning; and (vii) multi-modal ground-aerial exploration solutions. We demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.
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