Satellite Data Download Management with Uncertainty about the Generated Volumes

Earth observation satellites are space sensors which acquire data, compress and record it on board, and then download it to the ground. Because of the use of more and more sophisticated compression algorithms, the amount of data resulting from an acquisition is more and more unpredictable. In such conditions, planning satellite data download activities offline on the ground is more and more problematic. In this paper, we report the results of a work aiming at evaluating the positive impact of planning downloads onboard when the amount of data produced by each acquisition is known.The data download problem to be solved on board is an assignment and scheduling problem with unsharable resources, precedence constraints, time-dependent minimum durations, and a complex optimization criterion. The generic InCELL library is used to model constraints and criterion, to check non temporal constraints, to propagate temporal constraints, and to evaluate the criterion. On top of this library, greedy and local search algorithms have been designed to produce download plans with limited time and computing resources available on board.

[1]  Yves Crama,et al.  Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.

[2]  Cédric Pralet,et al.  Dynamic Online Planning and Scheduling Using a Static Invariant-Based Evaluation Model , 2013, ICAPS.

[3]  Cédric Pralet,et al.  Time-dependent Simple Temporal Networks: Properties and Algorithms , 2012, RAIRO Oper. Res..

[4]  Giovanni Righini,et al.  A Mathematical Programming Solution to the Mars Express Memory Dumping Problem , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Mark Woods,et al.  Autonomous science for an ExoMars Rover–like mission , 2009, J. Field Robotics.

[6]  David R. Thompson,et al.  Information-optimal selective data return for autonomous rover traverse science and survey , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Amedeo Cesta,et al.  An Innovative Product for Space Mission Planning: An A Posteriori Evaluation , 2007, ICAPS.

[8]  Tara A. Estlin,et al.  Oasis: Onboard autonomous science investigation system for opportunistic rover science , 2007, J. Field Robotics.

[9]  Tara Estlin,et al.  Oasis: Onboard autonomous science investigation system for opportunistic rover science: Research Articles , 2007 .

[10]  J. Lohn,et al.  A Comparison of Techniques for Scheduling Earth Observing Satellites , 2004, AAAI.

[11]  Rob Sherwood,et al.  The EO-1 autonomous science agent , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[12]  Stephen F. Smith,et al.  Generating Robust Schedules through Temporal Flexibility , 2004, ICAPS.

[13]  Amedeo Cesta,et al.  Generating High Quality Schedules for a Spacecraft Memory Downlink Problem , 2003, CP.

[14]  Gérard Verfaillie,et al.  Selecting and scheduling observations of agile satellites , 2002 .

[15]  Rob Sherwood,et al.  Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling , 2000, AIPS.

[16]  David Joslin,et al.  "Squeaky Wheel" Optimization , 1998, AAAI/IAAI.

[17]  Manuel Laguna,et al.  Tabu Search , 1997 .

[18]  Michael Pinedo.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[19]  Rina Dechter,et al.  Temporal Constraint Networks , 1989, Artif. Intell..

[20]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[21]  Deeparnab Chakrabarty,et al.  Knapsack Problems , 2008 .

[22]  Angelo Oddi,et al.  A Max-Flow Approach for Improving Robustness in a Spacecraft Downlink Schedule , 2004 .

[23]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[24]  F. Glover,et al.  In Modern Heuristic Techniques for Combinatorial Problems , 1993 .