Robotic prospecting for critical resources on the Moon, such as ilmenite, rare earth elements, and water ice, requires robust exploration methods given the diverse terrain and harsh environmental conditions. Although numerous analog field trials address these goals, comparing their results remains challenging because of differences in robot platforms and experimental setups. These missions typically assess performance using selected, scenario-specific engineering metrics that fail to establish a clear link between field performance and science-driven objectives. In this paper, we address this gap by deriving a structured framework of KPI from three realistic multi-robot lunar scenarios reflecting scientific objectives and operational constraints. Our framework emphasizes scenario-dependent priorities in efficiency, robustness, and precision, and is explicitly designed for practical applicability in field deployments. We validated the framework in a multi-robot field test and found it practical and easy to apply for efficiency- and robustness-related KPI, whereas precision-oriented KPI require reliable ground-truth data that is not always feasible to obtain in outdoor analog environments. Overall, we propose this framework as a common evaluation standard enabling consistent, goal-oriented comparison of multi-robot field trials and supporting systematic development of robotic systems for future planetary exploration.

翻译：在月球上对关键资源（如钛铁矿、稀土元素和水冰）进行机器人勘探，鉴于其多样化的地形和恶劣的环境条件，需要稳健的探索方法。尽管已有大量模拟实地试验致力于实现这些目标，但由于机器人平台和实验设置的差异，比较其结果仍然具有挑战性。这些任务通常使用选定的、特定场景的工程指标来评估性能，这些指标未能建立实地性能与科学驱动目标之间的明确联系。在本文中，我们通过从三个反映科学目标和操作约束的现实多机器人月球场景中推导出一个结构化的关键性能指标框架，来弥补这一差距。我们的框架强调效率、鲁棒性和精度方面依赖于场景的优先级，并明确设计用于实地部署的实际应用。我们在一个多机器人实地测试中验证了该框架，发现其在效率和鲁棒性相关的关键性能指标上实用且易于应用，而面向精度的关键性能指标则需要可靠的基准真值数据，这在户外模拟环境中并非总能获得。总体而言，我们提出该框架作为一个通用的评估标准，能够实现多机器人实地试验的一致、目标导向的比较，并支持未来行星探索机器人系统的系统性开发。