Mingyu Kim

k012123600@gmail.com

20-02-2001

Omni-Wheel-Based Robot

An omni-wheel is a specialized wheel with multiple small rollers mounted around its circumference at regular intervals.

Demo GIF
Structure of the omni-wheel

Each roller is angled relative to the wheel’s axis of rotation, enabling smooth lateral movement. Thanks to this unique structure, a robot can move freely in any direction—360 degrees—without changing its orientation. This enables excellent maneuverability and precise positioning, even in tight spaces.

\[ \begin{pmatrix} u_1\\[6pt] u_2\\[6pt] u_3 \end{pmatrix} = \frac{1}{r} \begin{pmatrix} -d & 1 & 0 \\[4pt] -d & -\tfrac12 & -\sin\!\bigl(\tfrac{\pi}{3}\bigr) \\[4pt] -d & -\tfrac12 & +\sin\!\bigl(\tfrac{\pi}{3}\bigr) \end{pmatrix} \begin{pmatrix} \omega_{bz}\\[4pt] v_{bx}\\[4pt] v_{by} \end{pmatrix} \]

    r: Wheel radius

    d: Distance from the robot’s center to each wheel

    Wheel mounting angles: \( \beta_1 = 0,\;\beta_2 = -\frac{2\pi}{3},\;\beta_3 = +\frac{2\pi}{3} \)

    ui: Rotational speed assigned to each wheel

The value of \(u_i\) obtained from the equation is normalized using a scaling factor and converted into a PWM signal, with the sign determining the rotation direction of each motor.

Demo GIF
Demonstration of omni-wheel-based motion

This omni-wheel robot was the first complete robotic system through which I directly experienced every stage from design and fabrication to communication, firmware development, simulation, and real-world testing. By handling everything from hardware configuration and sensor data collection to control algorithm tuning and ROS integration, I built a solid foundation in robotics.

Swerve Fusion Demo
Gazebo Simulation Demo

Through building the hardware, collecting sensor data, tuning control algorithms, and integrating with ROS, I gained foundational skills in robotics.

Demo GIF
Demonstration of DRL-based navigation system

This robot was initially developed as part of an undergraduate capstone design project and later served as a testbed for reinforcement learning research.

Demo GIF

After training the policy in a simulator, we transferred it to the real robot (simulation-to-reality) and compared the performance in autonomous navigation.

Through the process of designing reward functions and configuring environments, I gained practical insights into various aspects of autonomous control research.

GitHub: ver1 https://github.com/kMinsAlgorithm/ProjectUSAE

GitHub: ver2https://github.com/kMinsAlgorithm/OmniWheelRobot