Abstract
Upcoming space missions are requiring a higher degree of on-board autonomy operations to increase quality science return, to minimize close-loop space-ground decision making, and to enable new scenarios. Artificial Intelligence technologies like Machine Learning and Automated Planning are becoming more and more popular as they can support data analytics conducted directly on-board as input for the on-board decision making system that generates plans or updates them while being executed. This paper describes the planning and execution architecture under development at the European Space Agency to target this need of autonomy for the ops-sat mission to be launched in 2019.
J. Gorfer—Work performed at Esa under traineeship activity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
ops-sat launch date is currently scheduled for November 2019.
- 2.
It is worth noting that, during this scouting phase, the pictures will not be stored in the memory nor downloaded to the ground.
- 3.
Considering the distance between two adjacent images, \(\delta _{lat}\) and \(\delta _{long}\), and level the number of ‘circles’ around the target that should be analyzed, we have, \(\forall i,j =-level,..., level\):
$$\begin{aligned} x'=x+i*\delta _{lat} \end{aligned}$$$$\begin{aligned} y'= y+j*\delta _{long} \end{aligned}$$.
- 4.
The attitude defines configuration and orientation of the satellite.
- 5.
- 6.
contains, during and before are temporal constraints in Allen’s temporal logic [14] among the interval where the values occur.
References
ops-sat: OPS-SAT Web Site (2018). https://www.esa.int/Our_Activities/Operations/OPS-SAT
Muscettola, N., Nayak, P.P., Pell, B., Williams, B.C.: Remote agent: to boldly go where no AI system has gone before. Artif. Intell. 103, 5–47 (1998)
Chien, S., et al.: Using autonomy flight software to improve science return on earth observing one. J. Aerosp. Comput. Inf. Commun. 2, 196–216 (2005)
Chien, S., et al.: Onboard autonomy on the intelligent payload experiment CubeSat mission. J. Aerosp. Inf. Syst. 14(6), 307–315 (2017). https://doi.org/10.2514/1.I010386
Grandjean, P., Pesquet, T., Muxi, A.M.M., Charmeau, M.C.: What on-board autonomy means for ground operations: an autonomy demonstrator conceptual design. In: SpaceOps 2004 (2004)
nmf: Nanosat Mission Operation Framework Web Site (2018). https://nanosat-mo-framework.github.io/
apsi:APSI Software Distribution Web Site (2017). https://essr.esa.int/project/apsi-advanced-planning-and-scheduling-initiative
Muscettola, N.: HSTS: integrating planning and scheduling. In: Zweben, M., Fox, M.S. (eds.) Intelligent Scheduling. Morgan Kauffmann, Burlington (1994)
Frank, J., Jonsson, A.: Constraint based attribute and interval planning. J. Constraints 8, 339–364 (2003)
Fratini, S., Pecora, F., Cesta, A.: Unifying planning and scheduling as timelines in a component-based perspective. Arch. Control Sci. 18, 231–271 (2008)
Chien, S., et al.: A generalized timeline representation, services, and interface for automating space mission operations. In: Proceedings of the 12th International Conference on Space Operations, SpaceOps, AIAA (2012)
Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126, 183–235 (1994)
Baptiste, P., Pape, C.L., Nuijten, W.: Constraint-Based Scheduling. Kluwer Academic Publishers, Norwell (2001)
Allen, J.: Maintaining knowledge about temporal intervals. Commun. ACM 26, 832–843 (1983)
Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artif. Intell. 49, 61–95 (1991)
Vidal, T., Ghallab, M.: Dealing with uncertain durations in temporal constraint networks dedicated to planning. In: ECAI, pp. 48–54. Wiley, Chichester (1996)
Morris, P., Muscettola, N., Vidal, T.: Dynamic control of plans with temporal uncertainty. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence. IJCAI 2001, San Francisco, CA, USA, vol. 1, pp. 494–499. Morgan Kaufmann Publishers Inc. (2001)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870 (2007)
Martinez Heras, J., Donati, A.: Enhanced telemetry monitoring with novelty detection. AI Mag. 35, 37–46 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Fratini, S., Gorfer, J., Policella, N. (2019). On Board Autonomy Operations for OPS-SAT Experiment. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-22999-3_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22998-6
Online ISBN: 978-3-030-22999-3
eBook Packages: Computer ScienceComputer Science (R0)