Apresentador(a): Guilherme Marcos Neves

Título do Trabalho: Collision Avoidance Maneuvers Optimization Using Genetic Algorithm

Resumo: Collision avoidance maneuvers are crucial for satellite safe operations. The present work aims to use a genetic algorithm to optimize the consumption of fuel in collision avoidance maneuvers (CAMs) in a context where there are dangerous conjunctions between a satellite that have to keep safe and another space object. In this work, the GA’s fitness function returns the total maneuver delta-v, however, if the minimal miss distance between the satellite and the secondary object exceeds a established threshold, a penalty is imposed on the performance of the individual. It is considered a burn strategy: an impulse in the in-track direction, and four conjunctions potentially dangerous to be avoided. At final, we contend that efficient collision avoidance maneuver optimization planning can be achieved by using a heuristic algorithm.