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SUMMARY:Comparison of different optimization methods to construct an acqui
sition plan finding the best compromise between calculation time and algor
ithm performance
DTSTART;VALUE=DATE-TIME:20181109T083000Z
DTEND;VALUE=DATE-TIME:20181109T090000Z
DTSTAMP;VALUE=DATE-TIME:20200705T043213Z
UID:indico-contribution-3854@indico.esa.int
DESCRIPTION:Speakers: Cécile RENAUDIE (CNES)\nThe observation of celestia
l bodies other than Earth can without any doubt benefit from Earth observa
tion satellites. This study\, conducted for the latter\, can easily be app
lied to Phobos for example\, as part of the MMX (Martian Moons Explorer) p
roject.\n\nEarth observation satellites realize several acquisitions\, whi
ch are linked together through an acquisition plan. The calculation time n
eeded to construct such a plan is limited\; therefore the method usually e
mployed is the greedy algorithm which gives a solution quickly but not opt
imal. The temporal and kinematical constraints are numerous\, making this
a high combinatorial problem. The biggest time cost is the management of k
inematics\, especially checking if the acquisitions can be linked to each
other\, that is\, calculating the minimum duration of the rallying sequenc
e “attitude maneuver + acquisition”. In order to reduce the calculatio
n time\, the duration of each attitude maneuver and each acquisition can b
e calculated approximately\, by adding an error margin to the result in or
der to ensure the feasibility of the acquisition plan. Therefore\, a balan
ce must be found between the calculation time an optimization method leave
s to the kinematics and the margin associated to the needed approximation
. Typically\, an exact resolution method which leaves less calculation tim
e for kinematics computation needs a rough approximation\, and thus a big
margin\, which deoptimizes the calculated acquisition plan.\n\nA simplifie
d simulation model is used in order to quickly evaluate the quality of a p
lan constructed with a method (for example: greedy algorithm\, branch and
bound\, stochastic greedy algorithm\, genetic algorithm\, simulated anneal
ing\, taboo search)\, as well as the needed calculation time and therefore
the time left for kinematics computation. This model is based on a time w
indows representation.\n\nThe originality is to determine these elements a
ccording to different margins then determine which model for the calculati
on of kinematics\, with the associated margin\, gives the best results. \n
\n\n\n\n\nThe results are different depending on the studied context\; the
goal is to find the method that offers the best compromise between qualit
y of the plan and calculation time left to kinematics.\n\nhttps://indico.e
sa.int/event/224/contributions/3854/
LOCATION: Single 107
URL:https://indico.esa.int/event/224/contributions/3854/
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