Ice worlds are at the forefront of astrobiological interest because of the evidence of subsurface oceans. Enceladus in particular is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible. To enable such a mission, NASA’s Jet Propulsion Laboratory is developing a snake-like robot called Exobiology Extant Life Surveyor (EELS) that can navigate Enceladus’ extreme surface and descend an erupting vent to capture unaltered liquid samples and potentially reach the ocean. However, navigating to and through Enceladus’ environment is challenging: Because of the limitations of existing orbital reconnaissance, there is substantial uncertainty with respect to its geometry and the physical properties of the surface/vents; communication is limited, which requires highly autonomous robots to execute the mission with limited human supervision. Here, we provide an overview of the EELS project and its development effort to create a risk-aware autonomous robot to navigate these extreme ice terrains/environments. We describe the robot’s architecture and the technical challenges to navigate and sense the icy environment safely and effectively. We focus on the challenges related to surface mobility, task and motion planning under uncertainty, and risk quantification. We provide initial results on mobility and risk-aware task and motion planning from field tests and simulated scenarios.

中文翻译：

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EELS：具有任务和运动规划能力的自主蛇形机器人，用于冰雪世界探索

由于存在地下海洋的证据，冰世界处于天体生物学兴趣的前沿。土卫二在冰冷的卫星中尤其独特，因为已知的喷口系统可能与地下海洋相连，海水通过该系统被喷射到太空。现有的一项研究表明，将小型机器人送入喷口并直接采样海水是可能的。为了完成这样的任务，美国宇航局喷气推进实验室正在开发一种名为外星生物现存生命勘测者（EELS）的蛇形机器人，它可以导航土卫二的极端表面并下降到喷发口以捕获未改变的液体样本并有可能到达海洋。然而，导航并穿过土卫二的环境具有挑战性：由于现有轨道侦察的局限性，其几何形状和表面/喷口的物理特性存在很大的不确定性；通信是有限的，这需要高度自主的机器人在有限的人类监督下执行任务。在这里，我们概述了 EELS 项目及其开发工作，以创建具有风险意识的自主机器人来导航这些极端的冰雪地形/环境。我们描述了机器人的架构以及安全有效地导航和感知冰冷环境的技术挑战。我们重点关注与表面机动性、不确定性下的任务和运动规划以及风险量化相关的挑战。我们通过现场测试和模拟场景提供有关移动性和风险意识任务以及运动规划的初步结果。