Mission Planning on ESA’s Billion Star Surveyor Gaia Mission

mentioning

confidence: 99%

A comparative study of mission planning systems: from Earth Observation business to Interplanetary Missions.

Parra¹,

Ortiz

Sitjà³

2018

In Flight Evolution of Onboard Automation on ESA’s Gaia Mission

Milligan¹,

Collins²

2018

Self Cite

This paper details the progressive steps taken in enhancing the onboard automation of ESA's Gaia mission in response to in-flight experience. Tasked with mapping 1 billion stars to unprecedented precision (to the micro-arc-second level, comparable to the width of a smart phone on the Moon as viewed from Earth). ESA's Science cornerstone mission is expected to also discover and chart 100,000's of new objects including near Earth asteroids, exoplanets, brown dwarfs and quasars. After a flawless launch 19 Dec 2013, Gaia was brought the circa 1.5 million kms into L2 via a sequence of orbit transfer manoeuvres. Starting in parallel to this, and lasting 6 months, the full spacecraft was commissioned and brought gradually up to full performance. Since Q3 2014, Gaia has been in the routine operations phase, gathering the enormous Astronomical data set for the multiple map releases planned throughout the mission lifetime and beyond. During commissioning, and later routine operations, ground responded to a number of challenges with the aim of efficiently maximizing mission performance. Measures were taken to increase onboard autonomy in a number of areas. These have been of two main types; one being efficient increase of mission data return (e.g. due to onboard performance in some areas being higher than expected), and the second responding to repeatable anomalies (i.e. autonomously recovering and thereby limiting their impact). Such repeatable anomalies have no permanent impact on the performance of the mission and can have their origin in various subsystems in the space and/or ground segment. This paper details various examples of onboard autonomy enhancements that have been implemented since launch. Their design, validation and implementation are described, along with an assessment of some of the onboard PUS services used (e.g. OBCPs, event-action, TC sequences) with respect to their usefulness to ground operations teams. The trade-offs and logic used when deciding firstly what to automate (and what to leave to manual operations), and also where to automate (onboard or on ground using the closed loop MATIS system), is also presented. Specific examples in the paper will include: dealing with a significant increase of data volume (circa 45%) to be downlinked to ground; automatically coping with local bad weather events at the ground stations; dealing with repeatable onboard anomalies ranging from the relatively benign (those temporarily impacting the precise thermal balance) to the more severe (those that can trigger Safe Mode and produce data losses in the range of weeks).

Progress in Automated Ground Control of ESA’s Gaia Mission

Collins¹,

Qedar²,

Brach³

et al. 2018

Self Cite

Tasked with mapping one billion stars to unprecedented precision (to the tens of microarc-second level, comparable to the width of a coin on the Moon as viewed from Earth), ESA's Science cornerstone mission Gaia was launched on 19 December 2013. Mission operations are performed from ESA's Operations Centre (ESOC) in Germany, and after insertion into its operational Lissajous orbit around the Sun-Earth system Lagrange point two, plus completion of approximately six months of commissioning, Gaia has been operating in its routine science phase since July 2014. Following the first full sky survey it was observed that Gaia's Science return was larger than predicted and consequently there was a significant increase of data volume (circa 45%) to be downlinked. This was due to various factors, mainly Gaia's ability to observe stars fainter than the original magnitude 20 limit. In order to downlink this data in an efficient manner a stepwise approach was implemented involving taking unmanned ground station passes to downlink this extra science data. A simple solution using open loop automation was quickly implemented and gradually improved upon over time, adding robustness and closed loop functionality using ESA's automation software Matis. In addition, the desire to improve efficiency has been combined with the capabilities of Matis to allow daily tasks to be automated that would otherwise require the need of a dedicated person to be present on-console in the control room. This includes not only ground-based tasks such as checking for data archive completeness and requesting offline data retrievals to fill gaps, but also commanding of the on-board spacecraft subsystems to dump stored telemetry to ground and request reports of on-board anomalies. Matis is also used in combination with telemetry arriving from the ground station network using another piece of ESA software named LMS. Together they will be used to, amongst other things, monitor the power level from the spacecraft transponder and automatically stop the downlink of science data on-board to avoid science data loss. This paper describes the experience of the Gaia operations team in evolving from an almost exclusively manual operations environment to the large amount of automation in use today. The introduction of further automation tasks to improve efficiency, and their implications for system monitoring of multiple spacecraft covering the entire Astronomy division within ESOC, will also be discussed. Finally, the future extensions of automation concepts for Gaia and other flying and not-yet-flying ESA missions will be explored.