无需预设轨迹，智能体通过局部感知实现高效滑翔飞行
Learning step-level dynamic soaring in shear flow
- 基于深度强化学习，用局部状态反馈控制实现逐步决策
- 在多变风切变环境中实现稳定全向导航，无需周期性规划
- 揭示生物飞行与自主系统在复杂流场中的能量获取机制
动态滑翔可利用风切变持续飞行，传统认知将其视为周期性动作，依赖稳定的流场假设。但在真实非定常环境中，此类假设常不成立，引发对是否需显式周期规划的疑问。本文表明，动态滑翔可通过仅依赖局部感知的逐步状态反馈控制自然涌现，无需显式轨迹规划。我们采用深度强化学习方法，获得在多种风切变条件下均表现稳健的控制策略。所学行为形成结构化控制律，协调转向与垂直运动，呈现由能量提取与方向进展权衡驱动的双阶段策略。该策略具备跨条件泛化能力，复现了生物飞行与最优控制解的关键特征。研究揭示了动态滑翔背后的反馈控制结构，证明高效能量捕获飞行可源于与流场的局部交互，无需显式规划，为生物飞行与复杂流耦合环境下的自主系统提供新洞见。
原文摘要 · Abstract (English)
Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes stable flow conditions. In realistic unsteady environments, however, such assumptions are often violated, raising the question of whether explicit cycle-level planning is necessary. Here, we show that dynamic soaring can emerge from step-level, state-feedback control using only local sensing, without explicit trajectory planning. Using deep reinforcement learning as a tool, we obtain policies that achieve robust omnidirectional navigation across diverse shear-flow conditions. The learned behavior organizes into a structured control law that coordinates turning and vertical motion, giving rise to a two-phase strategy governed by a trade-off between energy extraction and directional progress. The resulting policy generalizes across varying conditions and reproduces key features observed in biological flight and optimal-control solutions. These findings identify a feedback-based control structure underlying dynamic soaring, demonstrating that efficient energy-harvesting flight can emerge from local interactions with the flow without explicit planning, and providing insights for biological flight and autonomous systems in complex, flow-coupled environments.
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