arXiv:2601.20529cs.ROcs.MA2026-01被引 2

为月球多机器人探测设计可落地的性能评估框架

A Practical Framework of Key Performance Indicators for Multi-Robot Lunar and Planetary Field Tests

  • 从三个真实月球任务场景出发,构建分优先级的评估指标体系
  • 实测验证框架易用性,效率与鲁棒性指标可行,精度需可靠真值数据
  • 适合未来行星探测机器人系统研发与测试对比

在月球上探测铁钛矿、稀土元素和水冰等关键资源,需要应对复杂地形和严酷环境。尽管已有大量类比实地试验,但因机器人平台和实验设置差异,难以进行有效比较。现有任务多采用特定场景的工程指标,无法清晰关联实际科学目标。本文基于三个反映科学目标与操作约束的真实多机器人月球场景,构建了一套结构化的关键绩效指标(KPI)框架,强调效率、鲁棒性和精度在不同场景下的优先级,注重野外部署的实际可用性。我们在一次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KPI上实用且易操作;而精度相关指标依赖可靠的地面真值数据,在户外类比环境中往往难以获取。整体而言,该框架可作为统一评估标准,实现多机器人实地试验的一致性、目标导向比较,推动未来行星探测机器人系统的系统化发展。

原文摘要 · Abstract (English)

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.

多机器人月球探测性能评估实地测试

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