Abstract

The Venus Express mission was launched in late 2005 after a very short development time. The spacecraft was successfully ingested in Venus orbit in April 2006 and operations started shortly after. The Science Operations are coordinated by the Venus Express Science Operations Centre (VSOC) located in Madrid, Spain. However most of the work is done across Europe, in the Principal Investigator (PI) institutes. In the case of the Venus Express Monitoring Camera (VMC), it is done from Lindau in Germany, with some support provided by ESA. The planning is divided in three main cycles, namely long, medium and short term planning. In the long term planning cycle, the high level requirements are derived, and the science objectives defined. This provides a skeleton to the medium term planning cycle where most of the activities take place. Finally, very close to the actual operations there is the short term planning cycle, where only small changes can take place, mainly in refining details such as exposure times. In VMC, from the beginning of 2007, a new system is being used, based in past knowledge acquired from previous ESA missions, and in particular SMART-1. Within this system in the Medium term plan, science events are derived from the Long term plan science requirements, thus providing windows of opportunity for the science to be conducted. These windows are defined by optimum observation conditions, where the parameters are the observation geometries, available resources such as data or power, and environment constraints such as the sun position. Not being a completely new approach on ESA missions, the attempt here is to re-use a system, with a completely different set of requirements, and show that with few adjustments such system can provide extremely good results. This approach also has the bonus of years of live test and debugging. On top of the re-use of the concept, tools are also re-utilized to a great extent. In this case benefiting from a similar approach on the software development of the ESA planetary missions where software is reused across missions. In this paper we will show how it is possible, following the approach described, to reduce the amount of workload, and consequently the costs of operations, while keeping the same level of achievement, or even better, as more time is available to dedicate to analyze the science data.

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