Earth observing satellites (EOS) orbit around the earth to perform observation tasks specified by users. The additional maneuverability resulting from higher degrees of freedom than nonagile EOS (N-AEOS) provides agile EOS (AEOS) a significantly larger visible time window to complete the tasks. As a consequence, the task scheduling for AEOS is much more computationally complex than N-AEOS. In this article, a mixed-integer nonlinear optimization problem is formulated to find a near-optimal task allocation for a realistic AEOS scheduling problem. The satellite resources, such as energy and memory constraints, are considered in this problem. A reward factor is used to address the requirement of multiple scans in order to complete a task. A probability factor is also taken into consideration to incorporate the uncertainty of successful scans due to external factors, such as cloud coverage. An elitist mixed coded genetic algorithm-based satellite scheduling (EMCGA-SS) algorithm is proposed to solve the formulated problem. EMCGA-SS is extended to elitist mixed coded hybrid genetic algorithm-based satellite scheduling by combining a hill-climber mechanism in order to have better initialization. Experimental results to illustrate the performance of the algorithms and a comparison with some widely used methodologies are also presented.


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    Title :

    Reward Factor-Based Multiple Agile Satellites Scheduling With Energy and Memory Constraints


    Contributors:


    Publication date :

    2022-08-01


    Size :

    2769212 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English