The GTOC9 competition requires the design of a sequence of missions to remove debris from the LEO orbit. A mission is a sequence of transfer of the spacecraft from one debris to another. Both missions and transfer must fulfill a set of constraints. The work presents the procedures to develop a solution for the GTOC9 problem (i.e the mission sequence) that does not violate constraints.The solution is obtained through an evolutionary algorithm that combines pre-computed basic missions stored in a database. The main objective of the algorithm is to minimize the overall cost of the solution, in order to maximize the competition score. The database of pre-computed missions is derived by connecting transfers stored in a database of transfers, through a combinatorial approach that considers the problem constraints. The database of transfer is formulated through the solution of a constrained minimization problem upon the control action (the magnitude of the overall impulsive velocity changes Δ V ). Only a subset of all possible transfers (selected on the basis of acceptable Δ V ), enters in the database.
GTOC 9: Results from University of Trento (team ELFMAN) / Bertolazzi, Enrico; Biral, Francesco; Ragni, Matteo. - In: ACTA FUTURA. - ISSN 2309-1940. - STAMPA. - 2018:11(2018), pp. 79-90. [10.5281/zenodo.1139258]
GTOC 9: Results from University of Trento (team ELFMAN)
Enrico Bertolazzi;Francesco Biral;Matteo Ragni
2018-01-01
Abstract
The GTOC9 competition requires the design of a sequence of missions to remove debris from the LEO orbit. A mission is a sequence of transfer of the spacecraft from one debris to another. Both missions and transfer must fulfill a set of constraints. The work presents the procedures to develop a solution for the GTOC9 problem (i.e the mission sequence) that does not violate constraints.The solution is obtained through an evolutionary algorithm that combines pre-computed basic missions stored in a database. The main objective of the algorithm is to minimize the overall cost of the solution, in order to maximize the competition score. The database of pre-computed missions is derived by connecting transfers stored in a database of transfers, through a combinatorial approach that considers the problem constraints. The database of transfer is formulated through the solution of a constrained minimization problem upon the control action (the magnitude of the overall impulsive velocity changes Δ V ). Only a subset of all possible transfers (selected on the basis of acceptable Δ V ), enters in the database.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione