TY - GEN
T1 - Artificial intelligence in support to space traffic management
AU - Vasile, Massimiliano
AU - Rodríguez-Fernández, Víctor
AU - Serra, Romain
AU - Camacho, David
AU - Riccardi, Annalisa
PY - 2018/6/1
Y1 - 2018/6/1
N2 - This paper presents an Artificial Intelligence-based decision support system to assist ground operators to plan and implement collision avoidance manoeuvres. When a new conjunction is expected, the system provides the operator with an optimal manoeuvre and an analysis of the possible outcomes. Machine learning techniques are combined with uncertainty quantification and orbital mechanics calculations to support an optimal and reliable management of space traffic. A dataset of collision avoidance manoeuvres has been created by simulating a range of scenarios in which optimal manoeuvres (in the sense of optimal control) are applied to reduce the collision probability between pairs of objects. The consequences of the execution of a manoeuvre are evaluated to assess its benefits against its cost. Consequences are quantified in terms of the need for additional manoeuvres to avoid subsequent collisions. By using this dataset, we train predictive models that forecast the risk of avoiding new collisions, and use them to recommend alternative manoeuvres that may be globally better for the space environment.
AB - This paper presents an Artificial Intelligence-based decision support system to assist ground operators to plan and implement collision avoidance manoeuvres. When a new conjunction is expected, the system provides the operator with an optimal manoeuvre and an analysis of the possible outcomes. Machine learning techniques are combined with uncertainty quantification and orbital mechanics calculations to support an optimal and reliable management of space traffic. A dataset of collision avoidance manoeuvres has been created by simulating a range of scenarios in which optimal manoeuvres (in the sense of optimal control) are applied to reduce the collision probability between pairs of objects. The consequences of the execution of a manoeuvre are evaluated to assess its benefits against its cost. Consequences are quantified in terms of the need for additional manoeuvres to avoid subsequent collisions. By using this dataset, we train predictive models that forecast the risk of avoiding new collisions, and use them to recommend alternative manoeuvres that may be globally better for the space environment.
KW - space traffic
KW - AI
KW - artificial intelligence
KW - astronautics
KW - collision avoidance manoeuvres
UR - http://www.proceedings.com/37978.html
M3 - Conference contribution book
AN - SCOPUS:85051422336
SN - 9781510855373
T3 - Proceedings of the International Astronautical Congress, IAC
SP - 3822
EP - 3831
BT - 68th International Astronautical Congress, IAC 2017
CY - Paris, France
T2 - 68th International Astronautical Congress: Unlocking Imagination, Fostering Innovation and Strengthening Security, IAC 2017
Y2 - 25 September 2017 through 29 September 2017
ER -