ECE 4160: Fast Robots Shao Stassen
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Shao Stassen

Senior at Cornell University studying Computer Science with a focus in Robotics

An inverted pendulum is nonlinear and unstable — left alone, the pole falls. Here an LQR controller, designed on the linearization about upright, computes the cart motion that keeps it balanced. I used this same principle in Lab 12.
Shao Stassen

Here on campus I am doing foundation model and diffusion policy research on the assistive robotic lab EmPRISE. I am also an Autonomous member of the Combat Robotics at Cornell Project Team. I will be updating my site through the semester to reflect my progress in Fast Robots - ECE4160.

Combat Robotics at CornellEmPRISE Lab

ECE 4160 Fast Robots Labs

  1. Lab 1: Artemis and Bluetooth

    Setting up the SparkFun RedBoard Artemis Nano, verifying the serial, analog-read and PDM microphone examples, then building a BLE command-and-notify link between the board and my laptop.

  2. Lab 2: IMU

    Integrating a 9DOF IMU, computing orientation from accelerometer and gyroscope data, characterizing noise in the frequency domain, and fusing the two estimates with a complementary filter.

  3. Lab 3: Time of Flight Sensors

    Permanently wiring two VL53L1X time-of-flight sensors, working around their shared I2C address, and streaming distance data over Bluetooth alongside the IMU.

  4. Lab 4: Motor Drivers and Open Loop Control

    Wiring dual motor drivers to the Artemis PWM pins, calibrating the drivetrain, and running open-loop maneuvers on the untethered car.

  5. Lab 5: Linear PID Control and Linear Interpolation

    A linear PID controller that drives the robot to a stop 304 mm from a wall, using linear extrapolation to decouple the control loop from the slower ToF sample rate.

  6. Lab 6: Orientation Control

    A yaw PID controller for in-place rotation with differential drive, mitigating gyroscope drift and derivative kick.

  7. Lab 7: Kalman Filter

    Estimating drag and mass to build a state-space model of the robot, then running a Kalman filter to interpolate between slow ToF readings.

  8. Lab 8: Stunts

    Engineering a two-phase state machine for the flip stunt with a Kalman-triggered transition, then pivoting to the drift task.

  9. Lab 9: Mapping

    Reusing the orientation PID controller to spin the robot in place at set poses and assemble a map of the arena from ToF scans.

  10. Lab 10: Grid Localization using Bayes Filter

    Implementing a Bayes filter in simulation over a (12, 9, 18) discretized grid, updating a belief over robot pose from ToF readings.

  11. Lab 11: Localization on the Real Robot

    Running the Bayes filter update step on the real car against a uniform prior, localizing from a single 360-degree scan of 18 range readings.

  12. Lab 12: Inverted Pendulum

    Balancing an inverted pendulum on the moving car with a Kalman filter and an LQR controller over the nonlinear cart-pole dynamics.