Adaptive Maneuvers for Aircraft Conflict Resolution Using Learning Theory, M.Sc. Thesis Sharif University of Technology ; Malaek, Mohammad Bagher (Supervisor)
Abstract
The problem of detection and resolution of aircraft collisions is very important due to the increasing demand for flights. Many algorithms have been developed in the past to increase automation in air traffic management and reduce the workload of air traffic controllers. These algorithms either have difficulty in generalizing to real problems or have high computational costs and do not correspond to the reality of the actual maneuvering characteristics of the aircraft performance. The aim of present study is to obtain dynamic maneuvers that are adaptive with reality and also optimal in terms of utilizing the capacity of flight sectors, so we propose Deep Reinforcement Learning(DRL) based on...
Cataloging briefAdaptive Maneuvers for Aircraft Conflict Resolution Using Learning Theory, M.Sc. Thesis Sharif University of Technology ; Malaek, Mohammad Bagher (Supervisor)
Abstract
The problem of detection and resolution of aircraft collisions is very important due to the increasing demand for flights. Many algorithms have been developed in the past to increase automation in air traffic management and reduce the workload of air traffic controllers. These algorithms either have difficulty in generalizing to real problems or have high computational costs and do not correspond to the reality of the actual maneuvering characteristics of the aircraft performance. The aim of present study is to obtain dynamic maneuvers that are adaptive with reality and also optimal in terms of utilizing the capacity of flight sectors, so we propose Deep Reinforcement Learning(DRL) based on...
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