用强化学习优化无人机设计，实测性能更优。
Performance-guided Task-specific Optimization for Multirotor Design
- 结合强化学习与贝叶斯优化，按任务性能驱动设计
- 优化后无人机在敏捷路径导航中表现优于传统构型
- 真实飞行验证了仿真到现实的可迁移性
本文提出一种面向任务的多旋翼微型飞行器设计优化方法。通过强化学习、贝叶斯优化和协方差矩阵自适应进化策略，仅依据闭环任务性能对飞行器设计进行优化。该方法系统探索电机布局的设计空间，同时满足可制造性约束并最小化气动干扰。结果表明，优化后的设计在敏捷航点导航任务中表现显著优于传统多旋翼构型，甚至超越文献中全驱动设计。我们构建并测试了一个优化后的实物样机，验证了该方法从仿真到现实的可迁移性。
原文摘要 · Abstract (English)
This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.
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