用可达性分析评估无人机风险，动态优化控制降低事故概率。
PRREACH: Probabilistic Risk Assessment Using Reachability for UAV Control
- 基于无人机动力学与可达性分析，全局评估飞行轨迹风险。
- 离线减险24%，在线减险53%，显著优于传统控制器。
- 适合需要实时风险管控的无人机系统开发与部署。
我们提出一种新型方法用于设计风险受限的无人飞行器（UAV）控制器。现有风险评估框架依赖于已知不同原因下事故发生的条件概率，但真实数据不足导致其难以实施；且缺乏风险缓解的控制方法。本文方法基于无人机动力学，采用可达性分析对所有可行飞行轨迹进行概率风险评估，并据此构建控制优化问题，仅最小化调整原有控制律即可使风险低于可接受阈值。该方法称为PRReach。借助公开可用的无人机动力学模型和开源空间数据，可实现离线预飞和在线飞行中的风险评估与缓解。在真实世界数据上的仿真测试表明，相较于经典控制器，PRReach控制器离线风险降低最多24%，在线降低最多53%。
原文摘要 · Abstract (English)
We present a new approach for designing risk-bounded controllers for Uncrewed Aerial Vehicles (UAVs). Existing frameworks for assessing risk of UAV operations rely on knowing the conditional probability of an incident occurring given different causes. Limited data for computing these probabilities makes real-world implementation of these frameworks difficult. Furthermore, existing frameworks do not include control methods for risk mitigation. Our approach relies on UAV dynamics, and employs reachability analysis for a probabilistic risk assessment over all feasible UAV trajectories. We use this holistic risk assessment to formulate a control optimization problem that minimally changes a UAV's existing control law to be bounded by an accepted risk threshold. We call our approach PRReach. Public and readily available UAV dynamics models and open source spatial data for mapping hazard outcomes enables practical implementation of PRReach for both offline pre-flight and online in-flight risk assessment and mitigation. We evaluate PRReach through simulation experiments on real-world data. Results show that PRReach controllers reduce risk by up to 24% offline, and up to 53% online from classical controllers.
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