Automatic Collision Avoidance And Navigation

The main goal of this paper is to permit the growth of air traffic, whilst maintaining or increasing Right safety levels through automation. This should assist pilots and air traffic controllers and increase aircraft autonomy. MAGENTA is a PhD thesis, providing a framework for developing automatic learning tools. This framework has proven to be suitable for the aeronautic environment because of its adaptability to dynamic environments. One objective is to automate present aircraft collision avoidance systems. Collision avoidance refers here to a variety of possible occurrences, including other aircraft, geography, buildings, and so on. Another specific objective is optimize present automatic steering systems, by means of which aircraft can follow a specific or select the best route from a starting point to the head of the runway at the destination airport. These two objectives are in line with current Free Flight and Free Route plans in both the United States and Europe. This paper includes testing these automatic steering, navigation and collision avoidance capabilities with a flight simulator in a similar way to how pilots learn and are tested.