Pose Estimation for Mobile Robots to Maximise Data Quality of Fixed-Focus Laser Diagnostics in Hazardous Environments

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    Abstract

    Characterisation of nuclear environments is critical
    for long term operation and decommissioning. Laser
    Induced Breakdown Spectroscopy (LIBS) is an example of a
    scientific instrument that could be deployed to aid in characterisation
    of unknown environments. LIBS consists of a high
    intensity pulsed laser being focussed down onto a target to
    create a plasma, and optical emission from the plasma is then
    used to determine elemental composition of unknown materials.
    For robots deployed with these instruments in extreme
    environments, mission time can be limited by hazards present
    such as radiation. Once deployed a robot must be able to
    collect the best data possible whilst maximising operational
    runtime. We present a data quality based probabilistic approach
    to robot pose estimation to maximise data quality, by
    considering optimum sensor placement whilst avoiding harmful
    environmental features such as radiation for a fixed-focus laser
    diagnostic such as LIBS. This approach is able to determine
    optimum robot poses for arbitrary targets in 3D for arbitrary
    diagnostic mounting with respect to the robot. The approach is
    able to avoid obstacles and avoid occlusion of the target by said
    obstacles. This can be used as part of autonomous investigation
    and characterisa
    Original languageEnglish
    DOIs
    Publication statusAccepted/In press - Oct 2018
    EventIEEE International Conference on Intelligent Robots and Systems (IROS) 2018 - Madrid, Spain
    Duration: 1 Oct 20185 Oct 2018
    https://www.iros2018.org/

    Conference

    ConferenceIEEE International Conference on Intelligent Robots and Systems (IROS) 2018
    Abbreviated titleIROS
    Country/TerritorySpain
    CityMadrid
    Period1/10/185/10/18
    Internet address

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