Robust by Co-Design: CAD-to-Control Evolutionary Optimization of Jet-Powered Humanoids

Abstract

This letter presents a co-design and optimization framework for improving robustness in an aerial humanoid equipped with variable turbine configurations. The approach integrates CAD-based modeling, domain randomization, and multi-objective evolutionary optimization to jointly explore turbine placement, jetpack geometry, and controller gains under uncertainty. We generate a family of candidate robot models using Uniform Latin Hypercube sampling and perturb nominal inertial properties and turbine thrust estimation parameters to emulate modeling and actuation uncertainty encountered in practice. We evaluate robustness through randomized assessments of trajectory-tracking and fuel-consumption objectives using an adapted NSGA-II algorithm that incorporates fairness-aware seed selection and multi-seed performance aggregation. Across these stochastic evaluations, the framework identifies design–controller pairs that maintain stable flight behavior while balancing energy efficiency. As all evaluated configurations are derived from CAD-generated models, the resulting solutions ensure physical consistency and manufacturability, providing a structured foundation for robustness-driven co-design of jet-powered aerial humanoid systems.