Paper ID: 556 The 16th International Conference on Space Operations 2020

Artificial Intelligence for Space Operations (AI) (13) AI - 2 ”Approaches to introduce AI in operations – II” (2)

Author: Dr. Lu´ısSim˜oes ESA - European Space Agency, Germany, [email protected]

Mr. Ben Day University of Cambridge, United Kingdom, [email protected] Ms. Vinutha Magal Shreenath KTH Royal Institute of Technology, Sweden, [email protected] Mr. Callum Wilson University of Strathclyde, United Kingdom, [email protected] Mr. Sylvester Kaczmarek Imperial College London, United Kingdom, [email protected] Mr. Bruno Sousa European Space Agency (ESA), Germany, [email protected] Mr. Alessandro Donati European Space Agency (ESA), Germany, [email protected]

LEARNING FROM HISTORY: SCORING & AUTOMATING SPACECRAFT CONSTELLATION SCHEDULES

Abstract Abstract Scheduling the Cluster II constellation is a time-consuming task with a high cognitive load. Besides the physical and operational considerations when booking ground-station passes, the objectives and organisational priority of the mission have changed over the twenty-year lifetime of the mission. This makes scheduling this particular mission a complex problem demanding expertise and the development of tacit knowledge of what makes a schedule, and the passes from which a schedule is built, ’good’. In this paper, we present an approach that combines scoring explicit objectives with a human decision making model to create schedules similar to those produced by human operators while optimising their objectives.

1 Background

The European Space agency (ESA) Cluster II constellation launched in 2000 and is in the 20th year of its 2 year mission. The 4 spacecraft in the constellation collect scientific data on the magnetic environment and its interaction with the solar wind. Over the mission lifetime, the data downlinked has changed as newer missions are launched. As such, understanding how to maximise scientific data downlinked at lower costs for operators and groundstations has become a priority. A classic Constraint Satisfaction Problem (csp) framework was adapted in an attempt to solve the Cluster II’s mission requirements, or at least diminish operator workload [?]. The constraint solver was rigorously tested and deployed in real time at the European Space Operations Centre (ESOC). Though the approach generated solutions that satisfied all constraints, it was perceived by the operators as being unusable. The feedback suggested that while the solutions themselves were not incorrect, they did not fit other criteria that the operators follow implicitly. Each operator cited impracticalities from varying

1 csp tool experiences. This pure systems engineering approach can be considered as too reductionist in separating and replacing an operator from the critical knowledge and environment they operate in [?].

2 Methodology

Before settling on any solution, we first had to analyse two key domains – the historical data provided by ESA Cluster II Operators, and human demonstrations of the thought process informing new schedules. The Cluster II operator team provided logs on key metrics relating to satellite mode, pass schedules, ground station costs, and Cluster II specific communications link prediction. Through several sessions, operators demonstrated how they schedule the Cluster II mission. By talking through how they used the ESA scheduler interfaces and, importantly, what were the ‘good’ and ‘bad’ metrics, we were able to understand the context behind the historical data provided. Combining these domains, together with modern analytics tools, enabled exploration of past performance and, ultimately, instruction as to how new scores can be used as measures. The scores are able to reduce the load on the operator, by depicting the ‘goodness’ of their decisions with respect to key objectives and can be combined to depict the overall ‘goodness’ of a decision.

3 Scoring Functions

Some objectives, such as minimising data loss, and efficiency, are present in all missions. This was further motivation to formalise these objectives as scores. Based on sessions with the operators we were able to describe those measures than are able to be elicited. We also formalised the concrete objectives of the mission. The definitions are below.

3.1 Cost efficiency This score is particularly dependent on how the mission is charged. In case of Cluster II missions, the cost of a pass is non-linear in time – a pass duration of 1 hour or less is rounded up to 1 hour and charged at the 1-hour rate; a pass duration of greater than 1 hour is charged at the rate of the duration of the pass plus an hour, i.e. a 2 hour pass is charged at three times the 1-hour rate. The cost efficiency of the a pass is therefore allocated and that is measured in downloaded data per unit cost. The download rate is also affected link quality , presence of extraneous events such as eclipse, where the pass may not be allotted may not be completely efficient.

3.2 Loss inefficient data collection Loss is the term used by the operators to refer to periods of downlink where the spacecraft storage is empty. Data can be downloaded faster than it is created, so downloading when the storage is empty represents an inefficiency. We formalise this as a score that is reduced by the fraction of pass time that is spent as loss.

3.3 Link budget alignment One of the factors in determining the ideal quality includes (but not limited to) the visibility of the spacecraft, the orientation of the craft relative to the dish, and the terrain local to the ground station. Our own system currently does not account for the possibility of atmospheric or local weather effects that may degrade the quality.

3.4 Fill level The fill level should be kept low. The fill level, f(t), is a real number between 0 (empty) and 1 (full). We measure the fill level over the visibility (or over any period of time) as the integral. The operators aim to keep the storage fill level below 75% (0.75) as a precaution / safeguard against data-loss owing to cancelled passes or some other issue (it allows a period after a cancelled pass that a new pass could be scheduled before data begins to be overwritten). We score how well this rule-of-thumb is complied with by considering how far the system goes over 0.75 and how long it is above the limit with the integral.

2 3.5 Eclipse loss During an eclipse, the spacecraft is completely solar powered as the battery is shutdown. This results in any data left in storage being lost. The associated cost is simply the scale of the loss.

3.6 Link sharing We split the scores into two parts: the creation of opportunities (by scheduling a pass such that a second pass can go overhead) and taking the opportunity (by scheduling the second pass appropriately). mspa is possible when the angular separation of two spacecraft (as they appear to a ground station) is less than 0.03 degrees.

3.7 Fragmentation The schedule has to be executed by groundstation operators at some point. There are four people working shifts for the Cluster II mission and it is important to be considerate of them and to devise plans that will allow easy shift scheduling. We also want to reduce the number of shifts required to operate a given number of passes. This means we want to clump the passes together (avoid fragmentation).

4 Conclusion

The scoring functions, which can quantify the objectives a space operator , can be used for purposes such as analytics of decisions, training of future operators, simulations of events, for scheduling etc. The scoring functions described in this abstract could act as an interface as they can be used for any mission in space, that would lead to better informed operators working in tandem with intelligent systems, that could learn from each other

Acknowledgments

The authors would like to acknowledge the support from Frontier Development Lab (FDL) and the European Space Agency (ESA).

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