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.

翻译：本文提出了一种分层式安全自主滑行框架，旨在增强多无人机系统的自动化地面运行能力。自主滑行问题因以下因素而变得尤为复杂：(i) 未知扰动（如侧风对飞机动力学的影响），(ii) 非计划障碍物导致的跑道侵入，以及(iii) 滑行道多个入口交汇处产生的时空冲突。为解决这些问题，我们提出了一个分层框架SAFE-TAXI，它将集中式时空规划与基于模型预测控制-控制屏障函数的分散式控制相结合，使飞机能够在避免交叉口冲突和非计划障碍物（如其他飞机或地面车辆）的同时安全通过滑行道。该框架在时间上将自主滑行问题解耦为冲突消解和运动规划两个层面。冲突消解以集中式方式处理，通过为每架飞机计算冲突感知的参考轨迹实现；而针对非计划障碍物的安全保证，则由以分散式方式实现的基于模型预测控制-控制屏障函数的控制器处理。我们通过数值仿真验证了所提框架的有效性，并利用小型固定翼测试平台Night Vapor进行了实验验证。