Genetic algorithms for optimal plane conflict resolution in air traffic

International audienceAt the dawn of civil aviation, pilots resolved conflicts themselves because they always flew in good weather conditions with low speed aircrafts. Nowadays, pilots must be helped by an air traffic controller on the ground who has a global view of the current traffic distribution in the airspace and can give indications to the pilots to avoid collisions. Solutions to conflicts are empirical, controllers are trained to react to certain types of conflicts and are limited by a workload. It is clear that if the ATC is overloaded, the sky is not. Conflict resolution is a trajectory optimization problem under constraints the complexity of which is so important that it has not been solved yet. Many attempts have been made to solve this problem with classical methods, such as gradient methods, reactive technics, expert systems, but most of them failed. In this paper, we show how genetic algorithms can be used to solve en-route aircrafts conflict automatically to increase Air Traffic Control capacity in high density areas. Our main purpose is to find out the global optimum and not only a suitable solution, in a real time situation, with conflict free trajectories that respect both plane and pilot performances

Genetic algorithms for optimal conflict resolution in Air Traffic

In this paper, we show how genetic algorithms can be used to solve en-route aircrafts conflict automatically to increase Air Traffic Control capacity in high density areas. AI topic: Genetic Algorithm, Optimization Domain Area: Air Traffic Control Status: Operational mock-up Effort: 2 man/year Impact: Optimization of aircrafts trajectories inside control sectors. 1 Introduction The CENA is the institute in charge of studies and research for improving the French ATC systems. Studies on the use of genetic algorithms for conflict resolution and air space sectoring have given encouraging results [3, 7, 8], and a new study has been funded to solve the conflict resolution for En Route Air Traffic Control. This paper summarizes the results of this study. At the dawn of civil aviation, pilots resolved conflicts themselves because they always flew in good weather conditions (good visibility) with low speed aircrafts. On the other hand, modern jet aircrafts do not enable pilots to resolve ..

Genetic algorithms for conflict resolution in Air Traffic

At the dawn of civil aviation, pilots resolved conflicts themselves because they always flew in good weather conditions with low speed aircraft. Nowadays, pilots must be helped by an air traffic controller on the ground who has a global view of the current traffic distribution in the airspace and can give indications to the pilots to avoid collisions. Solutions to conflicts are empirical, controllers are trained to react to certain types of conflicts and are limited by a workload. It is clear that if the ATC is overloaded, the sky is not. Optimal conflict resolution is a trajectory optimization problem under constraints the complexity of which is so important that it has not been solved yet. Many attempts have been made to solve this problem with classical methods, such as gradient methods, reactive technics, expert systems, but most of them failed. In this paper, we show how genetic algorithms can be used to solve en-route aircraft conflict automatically to increase Air Traffic Contro..

Genetic Algorithms for Optimal Plane Conflict Resolution

In this paper, we show how genetic algorithms can be used to solve en-route aircrafts conflict automatically to increase Air Traffic Control capacity in high density areas. AI topic: Genetic Algorithm, Optimization Domain Area: Air Traffic Control Status: Operational mock-up Effort: 2 man/year Impact: Optimization of planes trajectories inside control sectors