Learning and Autonomy for Extraterrestrial Terrain Sampling: An Experience Report from OWLAT Deployment

The exploration of ocean worlds stands as a pivotal element in humanity's exploration of our solar system, encompassing critical research objectives including the quest for potential signs of life and the comprehensive understanding of conditions fostering habitability [1], [2], [3]. Robotic exploration missions are essential for the exploration of potentially habitable ocean worlds. Past lander and rover missions including the Mars exploration program [4] and the Perseverance rover mission [5] are human-in-the-loop systems with expert teams on Earth supervising the terrain sampling process and controlling them based on the collected data. However, unlike Mars missions, many of the ocean world missions, including the Europa Lander mission concept [6], are anticipated to have short durations, on the order of tens of days, due to the intensity of the radiation environment, adverse thermal conditions, low availability of solar energy, and using battery as the sole power source. The limited mission duration combined with the long communication delays between Earth and the ocean worlds necessitates a high degree of autonomy for the lander's success [7]. The Europa lander's primary objectives include collecting terrain samples for in situ analysis of surface and sub-surface materials. Autonomy in terrain sampling missions is challenging due to the high degree of uncertainty in the surface topology at the landing site, terrain material properties, composition, and appearance. Constraints on the number of samples that can be analyzed in-situ, coupled with the risk of system failures, further limits the extent of exploration [8].