🤖 AI Summary
This study addresses the lack of an efficient, open-source, and physically credible multidisciplinary trade-off tool for lunar micro-rovers under 50 kg during their preliminary design phase. The authors present an open-source analytical framework that integrates terramechanics, mass estimation, power consumption, thermal survivability, and path planning. Capable of evaluating a single mission scenario in just 30 milliseconds, the tool supports NSGA-II-based multi-objective optimization and enables, for the first time, high-speed physics-driven trade-off analyses tailored to micro lunar rovers. Pareto fronts generated across diverse terrain scenarios demonstrate a median absolute error of 13.3% in mass prediction, with real-world designs closely approaching the optimization boundary—validating the method’s robustness. The results also reveal mission-dependent dominant constraints and challenge the conventional assumption that six-wheel configurations are inherently superior.
📝 Abstract
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.