Site ONERA
ONERA is opening a postdoc position for 2 years in the context of the European project DOMINO-E.
Start of contract: end 2023
Application deadline: 01/10/2023
Duration: 12 months, possibly extendable to 24 months
Net yearly salary: about 25 k€ (medical insurance included)
Keywords: Multi-agent resource allocation, planning & scheduling, online learning, reinforcement learning, multi-armed bandits, Earth observation satellite constellation, multi-mission
Profile and skills required: PhD in Computer Science, Artificial Intelligence or Operations Research with a strong publication record and a taste for theoretical and coding activities. Some prior knowledge in optimization, planning, scheduling, online learning, and reinforcement learning would be appreciated
( ! ) PhD students about to graduate (before end 2023) are welcome to apply!
Host laboratory: ONERA, Toulouse, France
Application: Applications including scientific CV, motivation letter, and letters from referees should be sent to Gauthier Picard (gauthier.picard@onera.fr) and Cédric Pralet (cedric.pralet@onera.fr)
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Context: In the context of the Horizon Europe DOMINO-E Innovation Action (https://domino-e.eu/), ONERA is involved in the development of novel techniques to demonstrate the feasibility of an innovative multi-mission federation layer for exploiting a set of space assets managed by various operators for various institutional and commercial applications. The goal of this federation layer is to efficiently use all these assets, so as to improve reactivity, persistence, precision and costs for various end-users. The federated layer will consist in smart services based on AI and machine learning, to be developed.
Proposed work: ONERA is involved in two main scientific tracks: multi-mission coverage and dispatch, and multi-mission communication booking. The first track aims to decide how to dispatch observations of large areas to different constellations, instead of a single one, as to optimize some performance criteria, such as the make-span and the quality of the images. Indeed, in order to minimize the time to deliver images for a specific large area requiring multiple snapshots, the idea is to query several missions. However, since the missions may not be legacy (not owned by the system operator), some information such as the workload and the precise schedule of the satellites are not available. In order to divide the large area into sub-areas, and to allocate such sub-areas to multiple missions, this requires building/learning a workload model or a query acceptance model to guide the dispatch decisions. This learning problem is not straightforward, since the workload is both space and time dependent. Moreover, the large area coverage problem itself is also a hard problem, addressed in the literature using multi-satellite coverage of discrete points of a large area [1, 2], multi-satellite coverage using 2D-strips over a continuous polygon [3, 4, 5, 6], and mono-satellite or multi-satellite area scanning strategies [7, 8]. Yet, there is still a lot of place for optimizing large area splitting methods, to get a faster global area coverage. Some works also consider several coverage requests simultaneously [9, 10, 11, 12, 13, 14, 15], and define criteria to arbitrate their scheduling. However, no work take into account multi-satellite observations together with the management of the current load of each mission or urgent requests. Moreover, interfaces with external systems are not really discussed, and dynamic dispatch (dispatch step-by-step to different missions, management of the long-term impact of the ongoing dispatch decisions, management of uncertainties about the cloud cover, etc.) is still an open issue.
The second track aims to decide how to book communication stations, as to optimize the data freshness. This is based on the novel concept of GSaaS (Ground Station/Segment as a Service), where mission operators can make use of external ground stations to communicate with the satellites. Indeed, the current concepts of operations are mostly based on legacy networks of ground stations (either proprietary or long-term booking) with high trust and satisfaction rates. The idea of using other stations, proposed by GSaaS providers, is to reduce the time to access data on ground thanks to non-legacy stations, instead of waiting to get access to legacy stations which may be not frequently accessible. But, here again, the workload of these GSaaS services is not available, and thus building a workload model or query acceptance model of such stations is required to book the proper stations, at the best price. While the problem of booking communication slots exists in the literature [16], no approach takes advantage of the novel concept of GSaaS.
This post-doctorate is a real opportunity to develop strong research and apply it in the context of an innovating research project. This research will develop and evaluate AI-based and optimization techniques (such as multi-agent resource allocation, reinforcement learning, online learning, reasoning under uncertainties, decomposition methods, metaheuristics, etc.) to address these two tracks, and integrate them into the DOMINO-E modular architecture, in close interaction with Airbus Defense and Space, Cap Gemini and ITTI development teams, in the context of the Horizon Europe DOMINO-E project.
References:
[1] Maillard, Adrien & Chien, Steve & Wells, Christopher. (2021). Planning the Coverage of Solar System Bodies Under Geometric Constraints. Journal of Aerospace Information Systems. 18. 1-18. 10.2514/1.I010896.
[2] Liu, Shufan & Hodgson, Michael. (2013). Optimizing large area coverage from multiple satellite-sensors. GIScience & Remote Sensing. 50. 10.1080/15481603.2013.866782.
[3] Niu, Xiaonan & Tang, Hong & Wu, L.. (2018). Satellite Scheduling of Large Areal Tasks for Rapid Response to Natural Disaster Using a Multi-Objective Genetic Algorithm. International Journal of Disaster Risk Reduction. 28. 10.1016/j.ijdrr.2018.02.013.
[4] Ntagiou, Evridiki & Iacopino, Claudio & Policella, Nicola & Armellin, Roberto & Donati, Alessandro. (2018). Ant-based Mission Planning: Two Examples. 10.2514/6.2018-2498.
[5] Chen, Yaxin & Xu, Miaozhong & Shen, Xin & Zhang, Guo & Zezhong, Lu & Xu, Junfei. (2020). A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping. Remote Sensing. 12. 344. 10.3390/rs12030344.
[6] Lenzen, Christoph and Dauth, Matthias and Fruth, Thomas and Petrak, Andreas and Gross, Elke Marie-Lena (2021) Planning Area Coverage with Low Priority. The 12th International Workshop on Planning & Scheduling for Space (IWPSS), 27-29. Jul. 2021.
[7] Ji, Hao-ran & Huang, Di. (2019). A mission planning method for multi-satellite wide area observation. International Journal of Advanced Robotic Systems. 16. 172988141989071. 10.1177/1729881419890715.
[8] Elly Shao, Amos Byon, Christopher Davies, Evan Davis, Russell Knight, Garrett Lewellen, Michael Trowbridge and Steve Chien (2018). Area Coverage Planning with 3-axis Steerable, 2D Framing Sensors. The 28th International Conference on Automated Planning and Scheduling, June 24–29, 2018, Delft, The Netherlands.
[9] Lemaître, M., Verfaillie, G., Jouhaud, F. Lachiver, J.-M., and Bataille, N. (2002). Selecting and scheduling observations of agile satellites. Aerospace Science and Technology, 6(5):367–381.
[10] Cordeau, J.-F. and Laporte, G. (2005). Maximizing the value of an Earth observation satellite orbit. Journal of the Operational Research Society, 56(8):962–968.
[11] W ang, P., Reinelt, G., Gao, P., and Tan, Y. (2011). A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation. Computers & Industrial Engineering, 61(2):322–335.s
[12] Tangpattanakul, P., Jozefowiez, N., and Lopez, P. (2015). A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite. European Journal of Operations Research.
[13] Zhu, W., Hu, X., Xia, W., and Sun, H. (2019). A three-phase solution method for the scheduling problem of using earth observation satellites to observe polygon requests. Computers & Industrial Engineering, 130:97–107.
[14] Berger, J., Giasson, E., Florea, M., Harb, M., Teske, A., Petriu, E., Abielmona, R., Falcon, R., and Lo, N. (2018). A Graph-based Genetic Algorithm to Solve the Virtual Constellation Multi-Satellite Collection Scheduling Problem. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–10.
[15] Zhibo, E., Shi, R., Gan, L., Baoyin, H., and Li, J. (2021). Multi-satellites imaging scheduling using individual reconfiguration based integer coding genetic algorithm. Acta Astronautica, 178:645–657.
[16] A. Maillard, G. Verfaillie, C. Pralet, J. Jaubert, I. Sebbag, F. Fontanari, and J. Lhermitte . Adaptable Data Download Schedules for Agile Earth-Observing Satellites, Journal of Aerospace Information Systems 2016 13:3, 280-300