Neural evolution for collision detection and resolution in a 2D free flight environment

During the last decade, air traffic movements worldwide have experienced a tremendous growth. Future air traffic management concepts such as Free Flight have been proposed to provide a means by which traffic flow efficiency can be increased. Under Free Flight, the current methods of airways and way-points for separation assurance won’t exist, providing an aircraft pilot more flexibility to follow more optimized routes given the ever changing nature of a flight plan (bad weather, delays, special use airspace, and runway closure/emergencies etc.). In order to compensate for the loss of airway structure, automated conflict detection and resolution tools will be required to aid pilots in ensuring safety and smooth flow of air traffic. The main challenge is to develop a robust and efficient algorithm to achieve real time performance for large and complex scenarios in a Free Flight Airspace. This paper investigates preliminary design and implementation issues in two dimensions for evolutionary techniques in collision avoidance. Such techniques may find solutions in much a shorter time than classical collision avoidance algorithms. The preliminary results demonstrate that an artificial neural network (ANN) can be trained to compute near optimal trajectories to solve two aircraft conflicts with high reliability while maintaining the mission trajectory towards the destination.