James Yang

James Yang

San Francisco Bay Area
995 followers 500+ connections

Activity

995 followers

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Experience

  • Cosmic Robotics Graphic

    Cosmic Robotics

    San Francisco, California, United States

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    Cambridge, Massachusetts, United States

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    Seattle, Washington, United States

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    Seattle, Washington, United States

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    Greater Seattle Area

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    Greater Seattle Area

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    Seattle, Washington, United States

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    Greater Seattle Area

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    Stennis Space Center, Mississippi

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    Greater Philadelphia Area

Education

  • University of Pennsylvania Graphic

    University of Pennsylvania

    3.81

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    Activities and Societies: Eta Kappa Nu Honor Society

    computer vision, machine perception, machine learning, control theory, embedded systems, mechatronics

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Publications

  • Orthogonality and Optimality in Non-Pheromone Mediated Foraging

    International Conference on Swarm Intelligence

    We describe the general foraging task, breaking it into two different subtasks: map-making and collection. Map-making is a task in which a map is constructed which contains the location(s) of an item or of items in the search area. Collection is the task in which an item is picked up and carried back to a central known location. We theoretically examine these tasks, generating minimal conditions for each one to be accomplished. We then build a swarm made up of two castes to accomplish this…

    We describe the general foraging task, breaking it into two different subtasks: map-making and collection. Map-making is a task in which a map is constructed which contains the location(s) of an item or of items in the search area. Collection is the task in which an item is picked up and carried back to a central known location. We theoretically examine these tasks, generating minimal conditions for each one to be accomplished. We then build a swarm made up of two castes to accomplish this, theoretically motivating the design of the swarm. Finally, we demonstrate that the swarm is optimal in the class of swarms utilizing line of sight communication, and give performance measures for open and closed search spaces.

    Other authors
    • Sanza Kazadi
    • James Park
    • Andrew Park
    See publication

Courses

  • Advanced Dynamics

    MEAM535

  • Advanced Robotics

    MEAM620

  • Electric and Magnetic Fields

    ESE310

  • Electrical Circuits and Systems I/II

    ESE215/216

  • Engineering Mechanics: Dynamics

    MEAM211

  • Engineering Probability

    ESE301

  • Fluid Mechanics

    MEAM302

  • Heat and Mass Transfer

    MEAM333

  • Intro to Dynamic Systems

    ESE210

  • Introduction to Mechanical Design

    MEAM101

  • Introduction to Robotics

    MEAM520

  • Learning in Robotics

    ESE650

  • Linear Control Theory

    ESE505

  • Machine Learning

    CIS520

  • Machine Perception

    CIS580

  • Machine Translation

    CIS526

  • Mechanics of Solids

    MEAM354

  • Mechatronics

    MEAM510

  • Non-Linear Systems and Control

    ESE617

  • Principles of Digital Design

    ESE170

  • Statics and Strengths of Materials

    MEAM210

  • Thermodynamics

    MEAM203

  • Vector/Tensor Analysis, Differential Geometry

    ENM511

  • Vibrations in Mechanical Systems

    MEAM321

Projects

  • Calico: A visual-inertial sensor calibration library

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    Calico is a lightweight visual-inertial calibration library that allows for rapid problem construction, debugging, and tool creation. Unlike other codebases that are limited to standalone calibration tools for specific hardware setups, Calico is a flexible library that enables building and modifying custom tools to suit your hardware requirements. If your calibration problem aligns with our geometry paradigm, there is no need to modify the underlying optimization.

    MIT License, source…

    Calico is a lightweight visual-inertial calibration library that allows for rapid problem construction, debugging, and tool creation. Unlike other codebases that are limited to standalone calibration tools for specific hardware setups, Calico is a flexible library that enables building and modifying custom tools to suit your hardware requirements. If your calibration problem aligns with our geometry paradigm, there is no need to modify the underlying optimization.

    MIT License, source code available at https://github.com/yangjames/Calico

  • Forward and Inverse Kinematics of PUMA robotic arm

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    • Performed forward and inverse kinematics on 7-DOF PUMA robotic arm

  • Haptic rendering of a virtual reality environment with SensAble PHANTOM robotic arm

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    • Rendered various shapes and force fields with various physical properties and texture in virtual environment
    • Demonstrated physical interpretation of a virtual environment using the PHANTOM haptic robotic arm

  • Monte-Carlo SLAM

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    Processed lidar, depth image, and odometry data to create a log-odds map of a humanoid robot's environment

  • Non-linear position control of a quadrotor

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    • Wrote a globally stable non-linear position controller for a quadrotor

  • Unscented Kalman Filter for orientation tracking

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    • Wrote a UKF for orientation estimation using accelerometer and gyro data from an IMU

Languages

  • English

    Native or bilingual proficiency

Organizations

  • IEEE

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    - Present

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