Six-Degree-of-Freedom Aircraft Landing Trajectory Planning with Runway Alignment

TL;DR

This paper introduces a novel trajectory optimization method for 6-DoF aircraft landing that incorporates runway alignment constraints and obstacle avoidance, using a sequential convex programming algorithm validated through extensive simulations.

Contribution

It formulates a new multi-phase trajectory planning scheme with runway alignment constraints and develops a fast convergence algorithm called xPTR for aircraft landing trajectory optimization.

Findings

The method effectively enforces runway alignment during final approach.

The xPTR algorithm accelerates convergence in trajectory optimization.

Numerical simulations demonstrate robustness across various initial conditions.

Abstract

This paper presents a numerical optimization algorithm for generating approach and landing trajectories for a six-degree-of-freedom (6-DoF) aircraft. We improve on the existing research on aircraft landing trajectory generation by formulating the trajectory optimization problem with additional real-world operational constraints, including 6-DoF aircraft dynamics, runway alignment, constant wind field, and obstacle avoidance, to obtain a continuous-time nonconvex optimal control problem. Particularly, the runway alignment constraint enforces the trajectory of the aircraft to be aligned with the runway only during the final approach phase. This is a novel feature that is essential for preventing an approach that is either too steep or too shallow. The proposed method models the runway alignment constraint through a multi-phase trajectory planning scheme, imposing alignment conditions exclusively during the final approach phase. We compare this formulation with the existing state-triggered constraint formulation for runway alignment. To solve the formulated problem, we design a novel sequential convex programming algorithm called xPTR that extends the penalized trust-region (PTR) algorithm by incorporating an extrapolation step to expedite convergence. We validate the proposed method through extensive numerical simulations, including a Monte Carlo study, to evaluate the robustness of the algorithm to varying initial conditions.