Computational design of planet regolith sampler based on Bayesian optimization

Abstract

Regolith sampling is one of the core missions in deep space exploration. The design, optimization, and fabrication of samplers are challenging tasks to meet the requirements of deep space exploration, often necessitating complex modeling with computer-aided design tools and demanding the expertise of experienced space engineers with lengthy design iterations. We propose an interactive design framework where designers collaborate with optimization tool to streamline the design process. With the operator adjusting the design goals, we introduce Bayesian optimization to automatically suggest the next sets of parameters to explore. This approach is suitable for optimization scenarios when the design goals cannot be well established as analytical functions and fewer design iterations are required. In this paper, we design and optimize the core structure of the sampler under both stress analysis and discrete element analysis, considering lower stress, greater sampling volume per unit power consumption, and smaller size. Both simulation and physical experimental results show that the design proposed by our framework outperforms existing designs with a small number of design iterations.