Alexander Leishman

Alexander Leishman

New York, New York, United States
2K followers 500+ connections

Activity

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Experience

  • River Graphic

    River

    San Francisco Bay Area

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    San Francisco Bay Area

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    Stanford, CA

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    San Francisco Bay Area

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    Palo Alto, CA & Taipei, Taiwan

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    United Kingdom

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    Collective Dynamics and Control Laboratory

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    Gulf of Mexico

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Education

  • Stanford University Graphic

    Stanford University

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    Activities and Societies: Stanford Review

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    Activities and Societies: Engineers Without Borders

Licenses & Certifications

Volunteer Experience

  • Engineer

    Compone, Peru

    Helped design and build water chlorination system with the UMD chapter of Engineers Without Borders.

Projects

  • Modeling and Simulation of a Micro-Helicopter in Wind

    This paper describes an 8-DOF dynamic model of a coaxial micro-helicopter flying through
    a steady, uniform wind. The helicopter is modeled as a six degree of freedom rigid body.
    There are an additional two degrees of freedom contributed to the model by the stabilizer
    bar located at the top of the helicopter rotor axis. A simulator using this model has been
    created in MATLAB in order analyze the behavior of the helicopter under various conditions.

    See project
  • Experimental Validation of Wind Estimation Using a Micro-helicopter

    Autonomous air and ocean vehicles operating in real-world environments will frequently
    encounter flowfields such as wind and ocean currents. The ability of a vehicle to estimate
    the spatial variation of surrounding flows is useful for the collection of the flow data itself
    and for the feedback of the data into individual or cooperative control algorithms. This paper
    presents a novel flowfield estimator and, along with a previously developed estimator,
    tests the capability of wind…

    Autonomous air and ocean vehicles operating in real-world environments will frequently
    encounter flowfields such as wind and ocean currents. The ability of a vehicle to estimate
    the spatial variation of surrounding flows is useful for the collection of the flow data itself
    and for the feedback of the data into individual or cooperative control algorithms. This paper
    presents a novel flowfield estimator and, along with a previously developed estimator,
    tests the capability of wind estimation under non-ideal conditions. The testing was done
    through numerical simulation and physical experimentation using tracking data of a microhelicopter
    flying through a fan-produced wind-field. Both estimators are based upon the
    model of a self-propelled Newtonian particle in a steady, uniform wind. Each estimator requires
    particle heading and position data for all time in order to produce a wind estimate.
    For error convergence, the estimators also require that the particle travel at a constant
    speed relative to the wind. Behavior of the estimators without the condition of wind uniformity
    is discussed. The estimators were tested experimentally, using a micro-helicopter
    and a fan-produced wind-field. Measurements of the position and orientation values of the
    helicopter along its flight path were taken with an infrared motion capture system. This
    data was run through the estimators and the resulting wind estimates are presented and
    compared to the measured wind-field. The trade-offs associated with each estimator are
    discussed and discrepancies between the theoretical model and the real-world conditions
    are addressed. Future estimator improvements and research directions are presented.

    See project

Languages

  • Chinese

    Elementary proficiency

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