Combining Embedded Systems, Sensor Fusion, and Smart Motor Control

Introduction

In a world of ever-evolving robotics, self-balancing robots have become a fascinating intersection of control systems, sensor fusion, and motor dynamics. Inspired by how humans maintain balance using our inner ear and muscle coordination, I decided to build my own self-balancing robot — a two-wheeled bot that remains upright while stationary or in motion.

This blog post breaks down me and my teams journey, design considerations, components used, and the key concepts that made it possible. Whether you're into robotics, embedded systems, or just curious about balance bots, there's something here for you.

The Team includes Aum Panchal(Team Leader, R&D), Ninad Pachupate(AutoCAD, Hardware), Mann Janodia(Hardware), Kevin Modi(Coder) and Yash Shah(Coder & Documentation)

🧠 The Concept Behind Self-Balancing Robots

At the core, a self-balancing robot is an inverted pendulum system. Instead of falling over, the robot constantly adjusts the wheel positions to counteract its tilt using a feedback control loop.

To achieve this, the robot must:

Detect its current angle (tilt).
Calculate the necessary correction.
Adjust motor speeds and direction in real-time.

This requires:

A reliable IMU (Inertial Measurement Unit) for angle detection.
A control algorithm (PID or complementary filter).
Precise motor control to react instantly.

🔧 Components Used

Over the course of development, I built two versions of the self-balancing robot — a basic prototype and an advanced version.

1. Basic Bot*:

MPU6050 (Accelerometer + Gyroscope)
ESP32 Microcontroller
Johnson DC Motors
L298N Motor Driver
IR Sensors & Ultrasonic Sensor (for additional obstacle detection)
Li-ion Battery Pack

Note* - This version used a basic PID control loop for balancing and featured simple line-following and obstacle avoidance capabilities.

2. Advanced Bot**:

MPU6050 IMU
STM32F411RE / ESP32
GM4108H-120T Gimbal Motors
SimpleFOC Shield v2.0.4 for Field-Oriented Control (FOC)
AMT103-V Rotary Encoders (for real-time motor position feedback)
LiPo Battery

Note** - This version incorporated precision motor feedback via encoders and used the SimpleFOC library for smooth, accurate motor control — a major leap from the first prototype.

⚙️ Control System

🔁 Feedback Loop

The robot uses the MPU6050 to continuously monitor its pitch angle. This value is processed by a PID (Proportional, Integral, Derivative) algorithm to calculate the corrective motor speeds needed to maintain balance.

🧮 Sensor Fusion

The MPU6050 provides raw accelerometer and gyroscope data. I used a complementary filter to fuse this data and obtain a stable and accurate angle estimate.

⚡ Motor Control

In the advanced version, Field-Oriented Control (FOC) was implemented using the SimpleFOC library and gimbal motors. This allowed:

Smoother torque control
Higher accuracy
More stable performance in dynamic environments

📐 Challenges Faced

Tuning the PID controller was crucial. Too aggressive, and the bot would over-correct; too slow, and it couldn’t respond in time.
Dealing with sensor drift and gyro noise required careful filtering.
Ensuring real-time motor response using efficient control logic and hardware timers.
Learning and integrating encoder feedback and FOC principles was one of the most rewarding and technically deep parts of the journey.

🧠 What I Learned

Fundamentals of inverted pendulum systems
Practical application of PID tuning and filtering techniques
Working with SimpleFOC, rotary encoders, and gimbal motors
Importance of low-latency, real-time control loops
How to debug complex embedded systems using a methodical approach

📹 Demo & Future Plans

This project gave me deep insights into robotics, but it's just the beginning. Future enhancements could include:

Remote control via BLE/Wi-Fi
Machine learning-based balance prediction
Integration with ROS (Robot Operating System) for navigation

Glimpses

✍️ Final Thoughts

Building a self-balancing robot is a challenging yet immensely rewarding experience. It blends theory with hands-on engineering, and every line of code or hardware tweak brings you closer to mastering balance.

If you’re passionate about robotics or embedded systems, I highly recommend diving into such a project. And if you have questions or want to collaborate, feel free to reach out!

🔗 Read the LinkedIn post here

Thanks for reading — and stay tuned for more innovations here on Yash Innovates 💡

Stay tuned as I dive deeper into IIoT, cloud, and AI integrations to bring more impactful solutions to life.

Connect with me on LinkedIn to follow my journey!

Yash Innovates

Beyond Stability: Engineering a High-Precision Self-Balancing Robot