SAFE-TAXI：一种具有运行时安全保障与冲突消解的分层式多无人机安全自动滑行框架
SAFE-TAXI: A Hierarchical Multi-UAS Safe Auto-Taxiing Framework with Runtime Safety Assurance and Conflict Resolution
AI总结：
本文提出SAFE-TAXI分层框架，结合集中式时空冲突消解与分散式MPC-CBF控制，解决多无人机自动滑行中的侧风扰动、障碍物入侵和交叉口冲突问题，并通过仿真与Night Vapor平台实验验证其有效性。
摘要：
我们提出了一种分层式安全自动滑行框架，以增强多个无人驾驶航空器系统（multi-UAS）的自动化地面运行能力。自动滑行问题尤其具有挑战性，原因在于：（i）未知扰动，例如影响飞行器动力学的侧风；（ii）由于计划外障碍物导致的滑行道入侵；以及（iii）滑行道多个入口点之间交叉口处的时空冲突。为解决这些问题，我们提出了一个分层框架，即SAFE-TAXI，它将集中式时空规划与基于分散式MPC-CBF的控制相结合，以安全地引导飞行器穿越滑行道，同时避免交叉口冲突和计划外障碍物（例如其他飞行器或地面车辆）。我们提出的框架在时间上将自动滑行问题解耦为冲突消解和运动规划两部分。冲突消解以集中方式处理，通过为每架飞行器计算具有冲突感知能力的参考轨迹来实现。相比之下，针对计划外障碍物的安全保障则由以分散方式实现的基于MPC-CBF的控制器来处理。我们通过数值仿真证明了所提出框架的有效性，并使用小型固定翼测试平台Night Vapor对其进行了实验验证。
英文摘要：
We present a hierarchical safe auto-taxiing framework to enhance the automated ground operations of multiple unmanned aircraft systems (multi-UAS). The auto-taxiing problem becomes particularly challenging due to (i) unknown disturbances, such as crosswind affecting the aircraft dynamics, (ii) taxiway incursions due to unplanned obstacles, and (iii) spatiotemporal conflicts at the intersections between multiple entry points in the taxiway. To address these issues, we propose a hierarchical framework, i.e., SAFE-TAXI, combining centralized spatiotemporal planning with decentralized MPC-CBF-based control to safely navigate the aircraft through the taxiway while avoiding intersection conflicts and unplanned obstacles (e.g., other aircraft or ground vehicles). Our proposed framework decouples the auto-taxiing problem temporally into conflict resolution and motion planning, respectively. Conflict resolution is handled in a centralized manner by computing conflict-aware reference trajectories for each aircraft. In contrast, safety assurance from unplanned obstacles is handled by an MPC-CBF-based controller implemented in a decentralized manner. We demonstrate the effectiveness of our proposed framework through numerical simulations and experimentally validate it using Night Vapor, a small-scale fixed-wing test platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
