Two-Wheel Balancing Robot with LG XBOOM Go: Build Tutorial

Added:

Frame Build
Wheel Setup
Electronics Test
Control Code
Robot Assembly
Balance Tuning

Frame Build

2:01
Playing Section
  • 1

    Aluminum frame assembled with plates and extrusion.

  • 2

    Motor mounts designed to slide for belt tensioning.

  • 3

    3D-printed blocks secure the steel axle.

Basic PID Control Theory: Understanding feedback loops, error calculation, and tuning parameters (Kp, Ki, Kd) for system stabilization.
Microcontroller Programming: Familiarity with C/C++ programming and hardware interfacing (I2C, SPI, PWM) using platforms like Teensy or Arduino.
Inertial Measurement Units (IMUs): Knowledge of how accelerometers and gyroscopes work, along with basic sensor fusion techniques (e.g., Complementary or Kalman filters) to estimate tilt angles.
DC Motor Dynamics and Encoders: Understanding how H-bridge motor drivers control speed/direction and how rotary encoders track wheel position and velocity.
State-Space Control and LQR: Transitioning from standard PID to Linear Quadratic Regulators (LQR) for more robust multi-variable control of the inverted pendulum system.
Dynamic System Modeling: Deriving the mathematical equations of motion for self-balancing robots using Newtonian or Lagrangian mechanics.
Autonomous Navigation and SLAM: Integrating sensors like LiDAR or depth cameras with ROS (Robot Operating System) to enable mapping and obstacle avoidance.
Embedded DSP and Real-Time Systems: Optimizing the control loop execution using Real-Time Operating Systems (RTOS) and advanced digital signal processing.
100.8K views3.5Klikes14:33@jamesbrutonOriginal Release: 2019-07-09

This video demonstrates how to build a two-wheel balancing robot using an LG XBOOM Go speaker as the head, featuring an aluminum and 3D printed frame with belt-driven wheels powered by motors controlled by a Teensy 3.6 microcontroller running a PID algorithm that uses data from an MPU 6050 inertial measurement unit to maintain balance; the key insight is that PID controllers for balancing robots typically require very high proportional (P) values (often ten times higher than derivative values) to enable rapid acceleration toward the upright position, with the D value used to dampen oscillations and achieve stable balancing.