Emergency trajectory generation for fixed-wing aircraft

Abstract

Loss of thrust emergencies, e.g. -- induced by bird strikes or fuel exhaustion -- give rise to the need for expeditiously generating feasible trajectories to nearby runways, in order to guide pilots. It is possible to pre-compute total loss of thrust trajectories from every point in a 3D flight plan, but dynamic factors which affect the feasibility of a trajectory, like partial power, wind conditions, and aircraft surface damage, cannot be predicted beforehand. We present a dynamic data-driven avionics software approach for emergency aircraft trajectory generation which can account for these factors. Our approach updates a damaged aircraft performance model during flight which is used for generating valid trajectories to a safe landing site. This model is parameterized on a baseline glide ratio~($g_0$) for a clean aircraft configuration, assuming best gliding airspeed on straight flight. The model predicts purely geometric criteria for flight trajectory generation, namely, glide ratio and radius of turn for different bank angles and drag configurations.

Our model can dynamically infer the most accurate baseline glide ratio of an aircraft from real-time aircraft sensor data.We further introduce a trajectory utility function to rank trajectories for safety, in particular, to prevent steep turns close to the ground and to remain as close to the airport or landing zone as possible. Wind can significantly affect a feasible gliding trajectory with respect to the ground by changing the shape of turns from circular to trochoidal, and by increasing or decreasing the effective ground speed. Thus, in the presence of wind, otherwise feasible trajectories may become infeasible. Therefore, we present an additional wind model that takes into account the observed baseline glide ratio of an aircraft and the horizontal wind vector~($\vect{\mbox{w}}$). Our dynamic data-driven system uses this wind model to generate wind-aware trajectories that are feasible in the presence of a steady, horizontal wind. As a use case, we consider the Hudson River ditching of US Airways 1549 in January 2009, using a flight simulator to evaluate our trajectories and to get sensor data (airspeed, GPS location, and barometric altitude). In this example, baseline glide ratios of 17.25:1 and 19:1 enabled us to generate trajectories up to 28 seconds and 36 seconds after the birds strike respectively. We were also able to generate a feasible wind-assisted trajectory when trajectories were not possible in the absence of wind. In our experiments, the computation time for a single trajectory ranged from 40 milliseconds to 60 milliseconds.