# Trajectory Optimization of Airport Surface Guidance Operations for Unmanned Guidance Vehicles

## TL;DR

A new three-stage framework for planning safe and efficient taxiing paths for unmanned guidance vehicles at airports is proposed, improving efficiency and reducing energy use.

## Contribution

A three-stage trajectory planning framework integrating improved A*, conflict prediction, and resolution for unmanned guidance vehicles is introduced.

## Key findings

- The proposed method improves operational efficiency by 43.65% compared to conventional operations.
- The framework reduces energy consumption by 34.52% compared to traditional guidance methods.
- Speed-profile design and airport rules significantly affect unmanned guidance vehicle trajectories.

## Abstract

What are the main findings?
A three-stage trajectory planning framework is proposed for airport surface unmanned guidance operations, which operates under operational safety constraints and integrates improved A* trajectory planning, time-window-based conflict prediction, and priority-driven conflict resolution.By incorporating speed-profile-based time calculation and spatiotemporal occupancy modeling of guidance vehicles and guidance units, the proposed method enables conflict-free trajectory generation while enhancing taxiing efficiency and reducing energy consumption.

A three-stage trajectory planning framework is proposed for airport surface unmanned guidance operations, which operates under operational safety constraints and integrates improved A* trajectory planning, time-window-based conflict prediction, and priority-driven conflict resolution.

By incorporating speed-profile-based time calculation and spatiotemporal occupancy modeling of guidance vehicles and guidance units, the proposed method enables conflict-free trajectory generation while enhancing taxiing efficiency and reducing energy consumption.

What are the implications of the main findings?
The results demonstrate that trajectory planning based on spatiotemporal state information—derived from speed profiles and time-window modeling—can effectively support safe and efficient unmanned guidance operations conducted by guidance vehicles and guidance units on airport surfaces.The proposed framework provides a practical decision-support framework for intelligent airport surface operations, supporting the deployment of electric unmanned guidance vehicles contributing to low-carbon and high-efficiency airport ground movement management.

The results demonstrate that trajectory planning based on spatiotemporal state information—derived from speed profiles and time-window modeling—can effectively support safe and efficient unmanned guidance operations conducted by guidance vehicles and guidance units on airport surfaces.

The proposed framework provides a practical decision-support framework for intelligent airport surface operations, supporting the deployment of electric unmanned guidance vehicles contributing to low-carbon and high-efficiency airport ground movement management.

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption.