Multi satellites scheduling algorithm based on task merging mechanism

Abstract Earth observation satellites are platforms equipped with optical instruments that orbit the earth to take photographs of specific areas at users’ requests. Compared with huge user requests, satellites are still scanty resources. For some task, the satellite has to roll its camera to take the desired image. However, many satellites are rigidly restricted on maneuverability. As a result, the performances of satellites are greatly confined. Therefore, we need a scientific observation plan to weaken the constraints arising from satellites’ poor slew ability. To solve the problem we present a multi satellites scheduling algorithm based on task merging mechanism. The algorithm partitions the problem into two sub-problems: task assignment and task merging. In task assignment, we propose an adaptive ant colony optimization algorithm to select specific time window for each task, creating a task list for each satellite. In task merging, we propose the concept of task combination and develop a dynamic programming algorithm to find the best merging plan for each satellite. The two sub-problems are logically coupled; a valid observation plan will be got after much iteration. Finally, a series of test examples are given out, which demonstrate our algorithm to be effective.

[1]  Gilbert Laporte,et al.  A heuristic for the multi-satellite, multi-orbit and multi-user management of Earth observation satellites , 2007, Eur. J. Oper. Res..

[2]  William J. Wolfe,et al.  Three Scheduling Algorithms Applied to the Earth Observing Systems Domain , 2000 .

[3]  Zhijun Yang,et al.  An ant colony optimization algorithm based on mutation and dynamic pheromone updating , 2004 .

[4]  Yuejin Tan,et al.  Joint Scheduling of Heterogeneous Earth Observing Satellites for Different Stakeholders , 2008 .

[5]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[6]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[7]  Minqiang Xu,et al.  Scheduling Observations of Agile Satellites with Combined Genetic Algorithm , 2007, Third International Conference on Natural Computation (ICNC 2007).

[8]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[9]  Wei-Cheng Lin,et al.  Daily imaging scheduling of an Earth observation satellite , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  Al Globus,et al.  Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach , 2002 .

[11]  Gilbert Laporte,et al.  Maximizing the value of an Earth observation satellite orbit , 2005, J. Oper. Res. Soc..

[12]  B. Bullnheimer,et al.  A NEW RANK BASED VERSION OF THE ANT SYSTEM: A COMPUTATIONAL STUDY , 1997 .

[13]  Brian Boffey,et al.  A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem , 2000, Eur. J. Oper. Res..

[14]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[15]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[16]  Ruan Qi-ming Multi-Satellite Scheduling Toward Spot and Polygon Observing Requests , 2009 .