用5000种设计+智能筛选，让喷气人形机器人飞得更稳更省力。
CAD-Driven Co-Design for Flight-Ready Jet-Powered Humanoids
- 通过修改肢体尺寸和喷口参数生成5000种设计方案，用聚类选出代表性模型。
- 在最小加速度轨迹下，实现轨迹跟踪误差小、能耗低的飞行控制。
- 适合机器人设计与飞行控制联合优化的研究者参考。
本文提出一种基于CAD的协同设计框架，用于优化喷气驱动的空中人形机器人以执行动态约束轨迹。从iRonCub-Mk3模型出发，采用实验设计（DoE）方法，通过调整肢体尺寸、喷口几何（如角度和偏移量）及整体质量分布，生成5000个几何多样且机械可行的设计。每个模型均通过CAD装配构建，确保结构有效性并兼容仿真工具。为降低计算成本并支持参数敏感性分析，使用K-means聚类对模型进行分组，选取代表性中心点进行评估。采用最小加速度轨迹作为飞行性能基准，提供位置与速度参考，用于基于动量的线性化模型预测控制（MPC）策略。随后利用NSGA-II算法进行多目标优化，联合探索设计中心点与MPC增益参数空间，目标是最小化轨迹跟踪误差与机械能消耗。该框架输出一组经验证的飞行就绪人形配置及其控制参数，为可实施的空中人形机器人设计提供系统化方法。
原文摘要 · Abstract (English)
This paper presents a CAD-driven co-design framework for optimizing jet-powered aerial humanoid robots to execute dynamically constrained trajectories. Starting from the iRonCub-Mk3 model, a Design of Experiments (DoE) approach is used to generate 5,000 geometrically varied and mechanically feasible designs by modifying limb dimensions, jet interface geometry (e.g., angle and offset), and overall mass distribution. Each model is constructed through CAD assemblies to ensure structural validity and compatibility with simulation tools. To reduce computational cost and enable parameter sensitivity analysis, the models are clustered using K-means, with representative centroids selected for evaluation. A minimum-jerk trajectory is used to assess flight performance, providing position and velocity references for a momentum-based linearized Model Predictive Control (MPC) strategy. A multi-objective optimization is then conducted using the NSGA-II algorithm, jointly exploring the space of design centroids and MPC gain parameters. The objectives are to minimize trajectory tracking error and mechanical energy expenditure. The framework outputs a set of flight-ready humanoid configurations with validated control parameters, offering a structured method for selecting and implementing feasible aerial humanoid designs.
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