This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.
翻译:本文介绍并讨论由TEAM CoSTAR(Collasution SubTerranean自治机器人)开发的算法、硬件和软件结构,这些算法、硬件和软件结构是在DARPA Subterranian挑战中竞争的。具体地说,它介绍了在隧道(2019年)和城市(202020年)的竞赛中所使用的技术,Costar在其中分别取得了第2和第1个位置。我们还讨论了COSTAR在Martiananalog表面和地下(拉瓦管)勘探中的演示。文件介绍了我们的自主解决方案,称为NeBula(网络信仰意识认知自主自主自主自主机器人)。NeBula是一个具有不确定性的框架,旨在通过在信仰空间(2019年)和城市(20202020年)进行推理和决策(Coopermissions分布于机器人和世界各邦的概率分布空间)中进行推理和决策,促进有弹性和模块的自主解决方案。我们讨论了Nebula框架的各个组成部分,包括:(一) 轮式定位定位和多式定位系统定位系统;(三) 弹性分析和地方规划;(四) 全球运动规划和探索规划和探索行为;(四) 全球运动规划和探索行为;(一) 风险规划;(一) 进行风险规划;(一) 轨道飞行任务规划;(六) 轨道规划;(六) 轨道规划;(六) 轨道规划;(六) 轨道规划;(六) 轨道规划;(六) 进行业绩演化(六) 进行业绩演化研究;(六)