1) The document discusses developing an automated method for generating escape paths for large aircraft taking off from challenging airports surrounded by terrain, in order to satisfy regulatory requirements while optimizing payload and engine performance. 2) It aims to build on robotic path planning techniques to address the complex problem in a unified mathematical framework, formalizing an approach to generate high-quality escape paths considering aircraft kinematics, terrain models, and constraints. 3) Evaluation of the model on sample real-world airports found it yielded superior payload capability, safer paths based on unique metrics, and high repeatability, while engine failure simulations challenged common regulatory beliefs and paved the way to regulatory changes and safer operations.

1. Large Aircraft Takeoff Path Optimization out of Terrain
Challenging Airports
Bertrand Masson
A thesis in fulfilment of the requirements for the degree of
Doctor of Philosophy
Abstract:
Large Aircraftoperatorsconductingregularpassengertransportmustsatisfyregulatoryrequirements
suchas consideringenginefailureattakeoff atthe worstpointof the takeoff roll.Suchconstraintscan
severely restrict commercial payload, for example in airports surrounded by high terrain. However,
current methods for the analysis of takeoff paths are largely manual and require significant time to
yield an allowable payload. These current methods may induce higher than necessary engine thrust
levels that increase engine maintenance cost and drive engine designers to design costlier higher
thrustengines.Incontrastresearchinrobotics,particularlyonunmannedaerial vehicles,hascreated
a wealth of automatic path planning techniques that enable high speed online guidance and
navigation and provide solutions to multi-constraint multi-cost path planning problems. Building on
roboticpathplanningtechniques,inthisresearchwe addressthiscomplex probleminordertoprovide
the aircraft performance engineer with automated methods of generating highquality escape paths
that combine complex aircraft kinematics, terrain models and regulatory constraints in a unified
mathematical framework.Specifically,we developageneral approachto the problem, formalize our
method, extending it out to engine failure on climb-out as well as in missed approach.
In order to test our approach over the complete range of world runways, we start off by creating a
runwayclassificationusing machine learningclassificationmethods,yieldingfourrunwaycategories:
Open,Coastal,ValleyandChannel.Classificationresultsshow thatclose to 30% of all worldrunways
present a challenge to current manual based methods, therefore justifying the creation of our
automated path generation. Runways are then selected out of these four categories to thoroughly

2. testthe model undervaryingconditions.Resultsfromourextensiveevaluationof itsimplementation
on a sample of real-world airports yields superior payload capability, safer paths based on a unique
set of metrics, and high repeatabilityof the path generator. Furthermore,engine failure simulations
in climbout and missed approach appear to challenge common regulatory beliefs, therefore paving
the way to regulatory changes and safer flight operations.