让无人机在未知复杂环境中安全飞行，靠的是把空间干扰变时间干扰。
From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting
- 把空间变化的干扰重参数化为时间变化，适配已有预训练安全函数。
- 硬件实验表明，相比基线方法，飞行稳定性提升显著。
- 适合无人机等高维系统在真实复杂环境中的自适应安全控制。
自动驾驶系统在城市空中交通等安全关键场景中的广泛应用，依赖于在不同环境条件下可靠、高效且安全的运行。基于价值函数的安全过滤器可最小化对原始控制器的修改，以确保安全性。近期方法利用离线学习的价值函数，将此类安全过滤器扩展至高维系统。然而，这些方法假设对所有可能的模型失配（即环境扰动）有详细先验知识——这种信息在真实场景中很少见。即使在如城市峡谷或工业区域等已知环境中，无人机仍会遭遇由载荷-无人机相互作用、湍流空气等因素引起的复杂空间变化扰动。本文提出SPACE2TIME，可在未知、空间变化的扰动下实现离线学习安全过滤器的安全自适应部署。核心思想是将空间扰动的变化重新参数化为时间变化，从而在在线运行时使用预计算的价值函数。我们在四旋翼无人机上通过大量仿真和硬件实验验证了SPACE2TIME，结果表明其性能显著优于基线方法。
原文摘要 · Abstract (English)
The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying environmental conditions. One such approach, value function-based safety filters, minimally modifies a nominal controller to ensure safety. Recent advances leverage offline learned value functions to scale these safety filters to high-dimensional systems. However, these methods assume detailed priors on all possible sources of model mismatch, in the form of disturbances in the environment -- information that is rarely available in real world settings. Even in well-mapped environments like urban canyons or industrial sites, drones encounter complex, spatially-varying disturbances arising from payload-drone interaction, turbulent airflow, and other environmental factors. We introduce SPACE2TIME, which enables safe and adaptive deployment of offline-learned safety filters under unknown, spatially-varying disturbances. The key idea is to reparameterize spatial variations in disturbance as temporal variations, enabling the use of precomputed value functions during online operation. We validate SPACE2TIME on a quadcopter through extensive simulations and hardware experiments, demonstrating significant improvement over baselines.
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