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

2025-03-06

AI Summary

This work addresses three key challenges in multi-UAV autonomous ground taxiing: crosswind disturbances, sudden intrusion of dynamic obstacles, and spatiotemporal conflicts at multi-entry intersections. To tackle these, we propose a hierarchical decoupled cooperative control framework: (i) an upper layer employing a spatiotemporal conflict graph model and centralized reference trajectory optimization for global, conflict-aware planning; and (ii) a lower layer implementing a decentralized MPC-CBF fused controller to ensure real-time local obstacle avoidance and robustness. Crucially, this architecture achieves the first safety-constrained *tight decoupling*—i.e., formal separation yet coordinated integration—of planning and control. Numerical simulations and flight experiments on the Night Vapor fixed-wing platform demonstrate 100% success rates in resolving intersection conflicts and avoiding collisions with dynamic obstacles, while overall taxiing success reaches 99.2%, significantly outperforming state-of-the-art approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Security and Privacy: Data transparency and provenance

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.

Problem

• Addresses unknown disturbances like crosswind affecting UAS dynamics.
• Resolves taxiway incursions from unplanned obstacles and spatiotemporal conflicts.
• Proposes a hierarchical framework for safe auto-taxiing with decentralized control.

Innovation

• Hierarchical framework for multi-UAS auto-taxiing
• Combines centralized planning with decentralized control
• MPC-CBF-based controller ensures obstacle avoidance