SAFE-TAXI: A Hierarchical Multi-UAS Safe Auto-Taxiing Framework with Runtime Safety Assurance and Conflict Resolution

TL;DR

SAFE-TAXI addresses multi-UAS auto-taxiing by decoupling conflict resolution from local tracking and safety assurance. It builds conflict graphs to derive conflict-free passing orders and time slots, then optimizes minimum-snap reference trajectories that are tracked by decentralized MPC-CBF controllers with dynamic-CBF safety constraints. The framework is validated through numerical simulations and real-world experiments on the Night Vapor platform, and integrated with a Smart Operation Center (SOC) for human-in-the-loop validation. Results show improved performance and reduced acceleration variance compared to reactive baselines, demonstrating practical viability for scalable airport-like operations.

Abstract

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.

Figures (6)

• Figure 1: Illustration of a mid-range fixed-wing aircraft (Windracers ULTRA) taxiing from the hanger to the runway.
• Figure 2: Overview of our proposed SAFE-TAXI framework. Using the position waypoints from a global path planner, our framework first constructs a conflict graph among aircraft with spatial conflicts while crossing an intersection. The conflict graph is resolved to generate a passing order and desired time slots for each aircraft crossing the intersection. Conflict-aware reference trajectories are then computed by solving a polynomial trajectory optimization with desired time slots as boundary conditions. Finally, utilizing a decentralized MPC-CBF controller, each aircraft tracks the conflict-aware reference trajectories, thus ensuring the safety of the aircraft against unplanned taxiway incursions.
• Figure 3: (a) An example scenario of multi-UAS auto-taxiing showing $4$ aircraft approaching an 8-way intersection with each aircraft in spatial conflict with one another, (b) Initial time slots estimated using the global path planner's position waypoints and the operating speed of the aircraft, (c) Construction and sequential resolution of a conflict graph to obtain conflict-free passing order, and (d) Final conflict-free time slots for each aircraft after conflict resolution.
• Figure 4: Simulation scenario for validation of our proposed SAFE-TAXI framework.
• Figure 5: Test platform for the experiment, Night Vapor, is a small-scale fixed-wing vehicle equipped with a flight controller unit, RF receiver and bind-and-play interface.