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    Increasing Mobile Robot Efficiency and Versatility Through Manipulation-Driven Adaptation1 

    Source: Journal of Mechanisms and Robotics:;2021:;volume( 013 ):;issue: 005:;page 050906-1
    Author(s): Kim, Raymond; Madabushi, Varun; Dong, Eric; Mazumdar, Anirban
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Terrestrial mobile robotics are crucial to a range of missions including planetary exploration, search and rescue, logistics, and national security. Many of these missions require the robot to operate on a broad variety ...
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    Enhancing Payload Capacity With Dual-Arm Manipulation and Adaptable Mechanical Intelligence 

    Source: Journal of Mechanisms and Robotics:;2021:;volume( 013 ):;issue: 002:;page 021012-1
    Author(s): Kim, Raymond; Balakirsky, Stephen; Ahlin, Konrad; Marcum, Matthew; Mazumdar, Anirban
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Individual manipulators are limited by their vertical total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential ...
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    Localization of Thermal Wellbore Defects Using Machine Learning 

    Source: Journal of Energy Resources Technology:;2022:;volume( 144 ):;issue: 009:;page 93005-1
    Author(s): Bruss, Kathryn; Kim, Raymond; Myers, Taylor A.; Su, Jiann-Cherng; Mazumdar, Anirban
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this study, a multistep, machine learning approach is used to localize two ...
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