RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
This paper introduces RoverDevKit, an open-source toolkit for exploring design trade-offs in lunar micro-rovers. It couples a closed-form wheel-soil model with bottom-up models for mass, solar power, thermal survival, and traversal to serve as a fitness function for multi-objective optimization. The authors demonstrate its ability to map trade-offs among distance traveled, vehicle mass, and slope capability across different mission terrains, noting that the wheel-soil model reproduces measured drawbar pull with accuracy consistent with typical 20-30% error for this class of model.
The authors present an open-source toolkit for conceptual design of lunar micro-rovers, coupling a wheel-soil model with mass and power models to evaluate trade-offs in vehicle sizing; reported accuracy is consistent with typical error bounds for this class of model.
Source: arXiv Robotics — research abstracts · Read original article ↗
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adds mass. We present RoverDevKit, an open-source tradespace exploration toolkit with an evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. The evaluator is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): a search comprising 3,000 mission evaluations takes under a minute on a single laptop core.
Open source S3
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sistent with the 20-30% error typically reported for this class of model, and the mass model predicts published 5-50 kg rover masses with median absolute percentage error of 10.5%. A comparison with five published rover designs in the same mass class shows that they lie near the predicted Pareto front.
Open source S5
adds mass. We present RoverDevKit, an open-source tradespace exploration toolkit with an evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. The evaluator is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): a search comprising 3,000 mission evaluations takes under a minute on a single laptop core.
Open source S3
We use it to map trade-offs among distance traveled, vehicle mass, and slope capability on mare, polar, highland, and crater-rim missions. The limiting factor varies by mission: energy storage at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim terrain. The wheel-soil model reproduces measured single-wheel drawbar pull from two independent datasets with accuracy con
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org