D. J. Frew

SMART-1 after lunar capture: First results and perspectives

SMART-1 is a technology demonstration mission for deep space solar electrical propulsion and technologies for the future. SMART-1 is Europe’s first lunar mission and will contribute to developing an international program of lunar exploration. The spacecraft was launched on 27th September 2003, as an auxiliary passenger to GTO on Ariane 5, to reach the Moon after a 15-month cruise, with lunar capture on 15th November 2004, just a week before the International Lunar Conference in Udaipur. SMART-1 carries seven experiments, including three remote sensing instruments used during the mission’s nominal six months and one year extension in lunar science orbit. These instruments will contribute to key planetary scientific questions, related to theories of lunar origin and evolution, the global and local crustal composition, the search for cold traps at the lunar poles and the mapping of potential lunar resources

Smart-1 science operations: Experiences and recommendations from ESA's first lunar mission

The European Space Agency’s Smart-1 spacecraft has been in orbit around the Moon since February 2005. The Smart-1 Science and Technology Operations Coordination Centre (STOC) for ESA’s first lunar mission is based within the Planetary Missions Division, making it the first in a series of interplanetary missions that will have their Science Operations Centre based in ESA. This small team of science operations engineers is responsible for proposing and coordinating all of the payload operations based on an established set of science-themes, prioritised targets and desired observation conditions identified in advance of the mission by the Smart-1 instrument scientists. This paper will summarise the initial goals of Smart-1, then reflect on the achievements and setbacks experienced within each phase of the mission. It will highlight the pro-active planning approach adopted by the STOC, which aims to optimise the science return of the Smart-1 mission. The lessons learned from this approach will be addressed, leading to recommendations for future missions from the perspective of the science operations manager.

SOPS: The Science Operations Planning System for the first ESA Lunar Mission SMART-1

One of the major tasks of the SMART-1 Science and Technology Operation Coordination of the European Space Agency is planning and coordinating the operational activities of the scientific payloads on SMART-1. To fulfil the scientific objectives of this mission and to achieve an optimal scientific output, it is necessary to analyse the possible science opportunities in each phase of the mission and to select and prioritize the operations of the scientific payloads with respect to the corresponding scientific goals. The Science Operations Planning System, SOPS, is the name of a set of new, generic software tools, which is being developed at the Research and Scientific Support Department of ESA for this purpose and is being currently used on Smart-1. It will help the science operation engineers and the payload teams by providing them with tools and information to ease and speed the process of decision making and planning. SOPS is composed of two major groups of components: The first group, the so called science operations knowledge base, provides the infrastructure to store all the relevant information about the operations of scientific payloads and various sophisticated interfaces to access this information through remote computers. The second group consists of a number of tools and clients, which access and use the information, contained in the knowledge base, to carry out different tasks such as analyzing/visualizing the possible science opportunities for scientific payloads, planning the communication passes and payload observations, archiving performed payload operations and generating interface documents and files for other existing planning tools at RSSD and for the flight control team.

Usage of Interactive Simulators in Support of Planetary Science Operations Planning

This paper presents a solution for interactivity between di erent simulators (geometry, environmental, payload, or spacecraft) and planning engines (machine to machine interaction: planners, schedulers, constraint checkers) or planning engineers (human to machine interaction) for supporting Planetary Science Operations Planning, demonstrating that the interactivity is possible and that it can be implemented within the Planetary Missions Science Ground Segments and easily extended to any Space Science Ground Segment.

Accomplishing the Science Objectives through the enhancement of the Science Planning Process for ESA Planetary Missions

The problem of planning the operations of scientific instruments on a planetary mission is characterised by the presence of a considerable number of candidate science opportunities at any given time, which are competing for limited and shared resources. One of the major challenges of any planning and scheduling problem is defining expedient criteria for the selection and prioritisation of activities, in order to achieve the objectives and to optimise the outcome of the planning process. Science operation planning for ESA planetary mission is no exception in this respect; mission science objectives need to be achieved inside the allocated mission duration through the scheduling of instrument observations within the available constraints and resources. In the centralised science-planning concept the process begins with the collection and organisation of the input planning information. Instrument teams submit observation requests that encapsulate all of the required information for planning a generic category of observations. Defining a link to one or more of the mission science objectives provides the justification for the inclusion of an observation. While a simple link can identify which scientific objectives a requested observation would contribute to the actual extent of the contribution requires the information and links to be elaborated. Another aspect that is not addressed sufficiently by simple assignment of requested observations to scientific objectives is the prioritisation of observations based on their context, that is consideration of previously executed or planned future observations of the same type. This paper will discuss how elaborated constraints and criteria for fulfilment of science objectives can be accommodated in the science planning data model. In particular it will examine the use of coverage requirements to define observation campaigns and their role in a prioritisation scheme used to converge on an observation schedule that optimises the contribution to the mission objectives, given the limited availability of spacecraft resources.

Science operations planning of the Rosetta encounter with comet 67P/Churyumov-Gerasimenko

Rosetta is a cornerstone mission of the European Space Agency (ESA). It was launched in March 2004 and will rendezvous with comet 67P/Churyumov-Gerasimenko (C-G) in 2014. Rosetta consists of an orbiter and a lander. Rosetta will meet Comet C-G early 2014 at a heliocentric distance of approximately 4 AU after wake up from a 2.5 year phase of deep space hibernation. The lander will be delivered to the surface in Nov. 2014 at around 3 AU from the sun, while the orbiter will continue to follow the comet on its orbit through perihelion until it reaches 2 AU outbound by end of 2015. The Science Operations and Data Handling Concept (SODH concept) deals with the 14 months between lander delivery and end of the nominal mission, the so called escort phase. That mission phase is extraordinarily complex: Approaching the sun the comet becomes increasingly active and its environment is expected to change dramatically and unpredictably. Therefore continuous monitoring of the comet (based on the science data returned) is required to mitigate risks on the spacecraft, mainly due to dust particles emitted from the nucleus. On the other hand, the evolving comet activity poses great scientific opportunities and payload operations are expected to react and adapt in response to these changing activities. In addition, the activity of the comet together with its small size (about 2 km radius) implies that the trajectory of the spacecraft relative to the nucleus may not be predictable for extended periods of time and that active orbit control will be required. The SODH concept foresees a closed loop system between operations planning and data analysis. Scientific operations planning is centralized at the Rosetta Science Operations Centre (RSOC), with an information repository at its core, containing operational inputs provided by the Principal Investigator (PI) teams that are responsible for the payload instruments. At the comet we expect to execute mostly predefined operation blocks. Changes in the comet environment and results of scientific observations feed back into the planning process. The planning process has already started with the baseline planning. It is based on the Rosetta Science Themes, representing the Science Objectives for Rosetta and the associated measurements by the various payload instruments. The instrument teams provide geometrical constraints (e.g. illumination requirements) and resource estimates (power, data volume, number of telecommands) needed for each measurement. The escort phase is divided into several phases. The proposed measurements are ordered based on their contribution to the science objectives to be covered during a given phase. The result will be the baseline plan of typical trajectories and pointing modes for each mission phase and an estimate of required resources, e.g. integration time and data volume. Expected conflicts and prioritization needs will also be identified in this stage. The

Science Opportunity Scheduling Based on Contextual Geometry Quantification for Planetary Missions

This paper presents a novel approach to science driven planning and scheduling of planetary science operations based on the use of contextual parameter quanti cation for modifying the prioritization of scienti c measurements given by the project, in order to better match the overall scienti c return of the mission as described by its science objectives and their decomposition into scienti c measurements.

BepiColombo MPO science planning concept

The proposed science operations concept for the BepiColombo Mercury Planetary Orbiter is presented. Building on lessons learnt from previous ESA planetary missions, the manner in which the mission constraints drive the planning concept and system architecture is discussed. The conceptual approach of a “Science Themes Table” is described as a means to define the science observations. The proposed concept shows how the science objectives continuously drive the planning of payload operations and how an intelligent science planning and data handling system assists the science operations team to schedule payload operations without losing sight of their associated higher-level objectives.