Gradient-based Nested Co-Design of Aerodynamic Shape and Control for Winged Robots

2026-03-06

AI Summary

This work addresses the performance limitations of conventional sequential design approaches for aerodynamic shaping and trajectory planning in wide-envelope flight tasks, which stem from neglecting their strong nonlinear coupling. The authors propose a gradient-driven nested co-optimization framework that, for the first time, integrates a differentiable neural surrogate model of aerodynamic forces directly into an optimal control solver, enabling end-to-end joint optimization of both the airframe geometry and motion planning for fixed-wing aircraft. By circumventing the simplifying assumptions typically imposed on either aerodynamic or control modules in traditional methods, the proposed approach achieves significantly superior performance over evolutionary algorithm baselines in perching and short-field landing tasks while substantially reducing computational cost.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• aerodynamic shape
• motion planning
• co-design
• winged robots
• flight envelope

Innovation

• gradient-based co-design
• aerodynamic shape optimization
• neural surrogate model
• optimal control
• winged robots