arXiv:2606.21755cs.ROastro-ph.IM2026-06

开源工具RoverDevKit可快速评估月球小车设计,助你找到最优配置。

RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers

论文配图:RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
图 1 · 摘自论文原文
  • 基于物理模型耦合移动、供电、热控等多因素,每任务仅需30毫秒
  • 不同地形下最优设计差异大:极区看储能,高地看牵引力,平原看续航
  • 四轮车在多数场景优于六轮,仅需越障时六轮才更优,适合航天设计者

月球小型探测车(<50公斤)的早期概念设计面临运动、能源、热控与质量的强耦合权衡,但现有工具多为专有、缺乏验证或速度不足。本文提出RoverDevKit,一个开源的分析评估工具,整合了地形力学、质量、能源、热生存和行驶能力模型,单次任务运行仅需30毫秒,足以直接作为多目标优化器的适应度函数。在月海、极地、高地和陨石坑边缘等多种任务场景下,通过NSGA-II算法得到的帕累托前沿显示,同一质量等级内,关键制约因素随任务类型变化:高纬度任务受能源存储主导,松软高地需更强坡面牵引力,而月海与陨石坑边缘任务则以行驶距离为核心。值得注意的是,在平滑土壤的里程-质量-坡度目标下,刚性四轮布局在全质量范围内均优于六轮;六轮摇臂-桅杆结构仅在需越障任务中进入帕累托解集。工具性能经组件与系统级验证:地形力学内核在两个独立数据集上与文献模型一致,底部推导质量模型对5–50公斤类探测车的预测误差中位数为13.3%,真实微车数据也接近优化前沿。即使传播地形力学误差,设计规律仍保持定性不变。工具、数据、验证文件及绘图脚本已公开发布。

原文摘要 · Abstract (English)

Pre-Phase-A design of lunar micro-rovers is dominated by tightly coupled mobility, power, thermal, and mass trades, yet conceptual-design tooling for the rapidly growing sub-50 kg class is typically proprietary, weakly benchmarked, or too slow to drive optimization. We contribute RoverDevKit, an open analytical evaluator coupling terramechanics, mass, power, thermal survival, and traverse that runs in 30ms per mission, fast enough to serve directly as a multi-objective optimizer's fitness function. Across mare, polar, highland, and crater-rim scenarios, NSGA-II Pareto fronts show that the binding design trade changes with mission profile within a single mass class: energy storage dominates at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim missions. Notably, rigid four-wheel layouts Pareto-dominate the full modeled mass range under smooth-regolith range-mass-slope objectives, contrary to the expectation that six-wheel architectures become optimal at heavier masses; six-wheel rocker-bogie layouts enter the Pareto set only once missions impose an obstacle-navigation requirement. The evaluator performance is benchmarked using both component and system checks: the terramechanics kernel matches measured single-wheel drawbar pull within the literature model-form band on two independent datasets, the bottom-up mass model predicts published in-class (5-50 kg) rover masses to 13.3% median absolute error, and a rediscovery check places real micro-rovers near the optimizer's fronts. Propagating the measured terramechanics error through the optimizer leaves the qualitative design rules unchanged. The tool, data, validation artifacts, and figure-generation scripts are released openly.

月球探测机器人设计多目标优化开源工具

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