联合优化翼型与飞行控制，提升扑翼机器人的任务表现。
Gradient-based Nested Co-Design of Aerodynamic Shape and Control for Winged Robots
- 用神经网络代理模型计算气动力建模，实现梯度驱动的嵌套协同设计。
- 在短距着陆和定点着陆任务中性能优于进化算法，耗时更少。
- 适用于复杂飞行包线任务，适合做飞行器一体化设计的研究者。
为特定任务（如悬停、运载）设计空中机器人，需根据任务需求定制其气动外形。对于涉及宽飞行包线的任务，传统先定外形再设计运动规划的顺序流程因两者间非线性耦合而可能次优。为此，本文提出一种通用、基于梯度的嵌套协同设计框架：运动规划器求解最优控制问题，气动力建模由神经网络代理模型提供。该方法可刻画空中机器人常见的亚音速流动条件，克服现有协同设计方法因简化假设导致的适用性局限——这些假设通常出于计算可行性，对规划器或气动模型施加了约束。我们在固定翼滑翔机的两个复杂动态任务（定点着陆与短距着陆）上验证了该方法，结果表明，相比进化基线，优化设计显著提升了任务性能，且计算时间大幅缩短。
原文摘要 · Abstract (English)
Designing aerial robots for specialized tasks, from perching to payload delivery, requires tailoring their aerodynamic shape to specific mission requirements. For tasks involving wide flight envelopes, the usual sequential process of first determining the shape and then the motion planner is likely to be suboptimal due to the inherent nonlinear interactions between them. This limitation has been motivating co-design research, which involves jointly optimizing the aerodynamic shape and the motion planner. In this paper, we present a general-purpose, gradient-based, nested co-design framework where the motion planner solves an optimal control problem and the aerodynamic forces used in the dynamics model are determined by a neural surrogate model. This enables us to model complex subsonic flow conditions encountered in aerial robotics and to overcome the limited applicability of existing co-design methods. These limitations stem from the simplifying assumptions they require for computational tractability to either the planner or the aerodynamics. We validate our method on two complex dynamic tasks for fixed-wing gliders: perching and a short landing. Our optimized designs improve task performance compared to an evolutionary baseline in a fraction of the computation time.
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