Coordinated planning of space-aeronautics earth-observing based on CSP theory

Air and space-based earth-observing sensors are instrumental in monitoring natural phenomenon. And space-aeronautics coordinated observation can complement each other. However, current operations involve “stovepipe” systems in which inefficiencies are inherent. Considering the difference between satellite task planning model and airplane task planning model, a coordinated optimization model of space-aeronautics earth-observing is constructed. On this basis, the multi agent systems (MAS) optimization algorithm based on Nash Equilibrium (NE) theory is proposed. Then we explore the iteration coordination mode of algorithm according to the Communicating Sequential Processes (CSP) method, and propose an adaptive “Super Step” iteration coordinate mode. The stability of proposed iteration coordinate mode is proved based on the CSP theory. At last, the proposed algorithm is used to solve the problem of the joint observation of space. Simulation and analysis show that the proposed algorithm can effectively improve the efficiency and optimization of iteration process.

[1]  Han-Lim Choi,et al.  Consensus-Based Decentralized Auctions for Robust Task Allocation , 2009, IEEE Transactions on Robotics.

[2]  L. Darrell Whitley,et al.  AFSCN scheduling: How the problem and solution have evolved , 2006, Math. Comput. Model..

[3]  Nicolas Zufferey,et al.  Graph colouring approaches for a satellite range scheduling problem , 2008, J. Sched..

[4]  Tal Shima,et al.  Unmanned Aerial Vehicles Cooperative Tracking of Moving Ground Target in Urban Environments , 2008 .

[5]  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).

[6]  C. A. R. Hoare,et al.  Communicating sequential processes , 1978, CACM.

[7]  Jin-Kao Hao,et al.  Upper Bounds for the SPOT 5 Daily Photograph Scheduling Problem , 2003, J. Comb. Optim..

[8]  K.V. Katsikopoulos.  $(P, p)$ Retraining Policies , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Glaydston Mattos Ribeiro,et al.  Strong formulation for the spot 5 daily photograph scheduling problem , 2010, J. Comb. Optim..

[10]  Yoav Shoham,et al.  Simple search methods for finding a Nash equilibrium , 2004, Games Econ. Behav..

[11]  Han-Lim Choi,et al.  Real-Time Multi-UAV Task Assignment in Dynamic and Uncertain Environments , 2009 .

[12]  Ramesh Subramonian,et al.  LogP: a practical model of parallel computation , 1996, CACM.

[13]  Chong Wang,et al.  A Distributed Cooperative Dynamic Task Planning Algorithm for Multiple Satellites Based on Multi-agent Hybrid Learning , 2011 .

[14]  Rob Sherwood,et al.  An autonomous Earth observing sensorweb , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Christoph Stasch,et al.  New Generation Sensor Web Enablement , 2011, Sensors.

[16]  Neil Immerman,et al.  The Complexity of Decentralized Control of Markov Decision Processes , 2000, UAI.