RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
Abstract
Lunar micro-rovers under 50 kg are a rapidly growing vehicle class, yet open, benchmarked tools for running optimization across design trades remain scarce. Rover sizing is a highly coupled problem with a feedback loop specific to surface vehicles: a heavier rover sinks deeper into loose regolith, deeper sinkage costs more power, more power requires a larger array, and the array adds mass. We present RoverDevKit, a tradespace exploration toolkit with an open evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. It is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): one search of 3000 mission evaluations takes under a minute on a single laptop core. We use it to map the trades among distance traveled, vehicle mass, and slope capability on mare, polar, highland, and crater-rim missions. Across those missions the limiting factor changes: energy storage at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim terrain. The wheel-soil model matches measured single-wheel drawbar pull on two independent datasets to within the 20-30% error typically reported for this class of model, and the mass model predicts published 5-50 kg rover masses to 10.5% median absolute error. A check against five published in-class designs places them near the designs the optimizer selects. Re-running the search after applying that wheel-soil comparison error leaves these conclusions unchanged.
Related
- meer21cm: an Analysis Pipeline and Comprehensive Toolkit for HI Intensity Mapping
- Sequince: a modular sequential Monte Carlo toolkit
- GIMLI: a toolkit connecting linear and non-linear magnetohydrodynamic regimes
- A Two-Stage Physically Grounded Optical Design Framework with Application to a Multi-Channel Focal Reducer
- AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
BibTeX
@misc{reifschneider2026roverdevkit,
title = {{RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers}},
author = {Jon Reifschneider},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.21755},
eprint = {2606.21755},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.21755},
}
RIS
TY - UNPB
TI - RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
AU - Jon Reifschneider
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2606.21755
UR - https://arxiv.org/abs/2606.21755
AB - Lunar micro-rovers under 50 kg are a rapidly growing vehicle class, yet open, benchmarked tools for running optimization across design trades remain scarce. Rover sizing is a highly coupled problem with a feedback loop specific to surface vehicles: a heavier rover sinks deeper into loose regolith, deeper sinkage costs more power, more power requires a larger array, and the array adds mass. We present RoverDevKit, a tradespace exploration toolkit with an open evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. It is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): one search of 3000 mission evaluations takes under a minute on a single laptop core. We use it to map the trades among distance traveled, vehicle mass, and slope capability on mare, polar, highland, and crater-rim missions. Across those missions the limiting factor changes: energy storage at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim terrain. The wheel-soil model matches measured single-wheel drawbar pull on two independent datasets to within the 20-30% error typically reported for this class of model, and the mass model predicts published 5-50 kg rover masses to 10.5% median absolute error. A check against five published in-class designs places them near the designs the optimizer selects. Re-running the search after applying that wheel-soil comparison error leaves these conclusions unchanged.
ER -