Path planning for emergency landings

Abstract

In the event of critical failures that compromise an aircraft's ability to remain airborne, flight crews face significant challenges in planning and executing an emergency landing. Currently, there is minimal flight deck support to assist crews in navigating this high-workload and high-risk situation. To maximise the probability of a safe landing, the development of onboard crew support systems that assist the pilots in making correct and timely decisions under high workload is critical. Such systems must consider a range of factors including proximity to potential landing sites, remaining aircraft performance, weather conditions, and terrain profiles. This project focuses on planning the descent trajectory to a predefined location in emergency scenarios involving a complete loss of engine thrust, requiring the aircraft to glide to the landing site without engine power. The aim of this dissertation is to compare two distinct approaches to trajectory planning algorithms: the Particle Swarm Optimization (PSO) and Non-Linear Programming (NLP). Both optimisers utilise the Dubin’s path to generate the lateral path of the aircraft by adjusting the location of predefined waypoints. The optimisation process seeks to identify an optimal descent trajectory that satisfies several operational and performance-related constraints. Utilizing nonlinear and linear constraints for the NLP and penalty functions for the PSO, the algorithms managed to identify valid trajectories that allow the aircraft to arrive at the target location at the correct aircraft energy level required for subsequent landing. Notably, each algorithm demonstrated unique strengths, with the NLP often generating slightly preferable paths compared to those produced by the PSO.