The success of a landed space exploration mission depends largely on the final landing site. Factors influencing site selection include safety, fuel-consumption, and scientific return. This paper addresses the problem of selecting the best available landing site based on these factors in real-time during autonomous spacecraft descent onto a planetary surface. The problem is modeled probabilistically using Bayesian Networks (BNs). BNs provide a means of representing the causal relationships between variables that impact the quality of a landing site. The final landing site is determined via probabilistic reasoning based on terrain safety derived from on-board sensors, available fuel based on spacecraft descent dynamics, and regions of interest defined by mission scientists.


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    Title :

    A Bayesian Framework for Landing Site Selection During Autonomous Spacecraft Descent


    Contributors:

    Conference:

    Intelligent Robots and Systmes Conference (IROS) ; 2006 ; Beijing, China


    Publication date :

    2006-01-01


    Type of media :

    Preprint


    Type of material :

    No indication


    Language :

    English





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