It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the “Parrot AR.Drone2.0” quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Combining temporal planning with probabilistic reasoning for autonomous surveillance missions


    Beteiligte:
    Bernardini, Sara (Autor:in) / Fox, Maria (Autor:in) / Long, Derek (Autor:in)

    Erscheinungsdatum :

    2017-01-01


    Anmerkungen:

    Bernardini , S , Fox , M & Long , D 2017 , ' Combining temporal planning with probabilistic reasoning for autonomous surveillance missions ' , Autonomous Robots , vol. 41 , no. 1 , pp. 181-203 . https://doi.org/10.1007/s10514-015-9534-0



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    006 / 629




    AUTONOMOUS VEHICLE FOR SURVEILLANCE MISSIONS IN OFF-ROAD ENVIRONMENT

    Naranjo, José E. / Clavijo, Miguel / Jiménez, Felipe et al. | British Library Conference Proceedings | 2016


    AUTONOMOUS UAV TEAM PLANNING FOR RECONNAISSANCE MISSIONS

    Szczerba, R. J. / Garrison, D. J. / Temullo, N. et al. | British Library Conference Proceedings | 2003


    Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning

    Li, Yang / Lu, Xiao-Yun / Wang, Jianqiang et al. | IEEE | 2020