Overview

Self Balancing Robot is an embedded systems project focused on keeping a two-wheel robot upright using sensor feedback and control logic.

It represents a different side of full stack learning: working close to hardware, reading sensor data, and translating real-world motion into code.

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Problem

A self-balancing robot is unstable by default. Small changes in angle, speed, or sensor noise can quickly make the robot fall if the control loop is not responsive.

• Sensor data needs to be read continuously
• Balance correction must happen quickly
• Motor output needs to respond smoothly
• Embedded code should stay understandable for tuning

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Solution

The project uses C++ and embedded control logic to read motion-related data, calculate balance correction, and control the robot's movement.

The implementation focuses on the feedback loop: measure the robot state, decide the correction, and apply motor output repeatedly.

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Architecture

Sensor Layer

• Reads orientation or motion-related input
• Provides state for the control loop
• Handles noisy real-world signals

Control Layer

• Balancing logic
• Correction calculation
• Tuning-friendly structure for control behavior

Hardware Layer

• Motor control output
• Embedded runtime constraints
• Real-world testing and adjustment

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Key Features

Balancing Logic

Implements the core feedback behavior needed to keep the robot upright.

Sensor Integration

Connects software behavior to physical sensor input.

Embedded Control

Uses C++ to manage hardware-facing logic in a resource-conscious environment.

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Challenges

Real-World Noise

Unlike pure web projects, hardware inputs can be inconsistent and require tuning.

Timing

The control loop needs to run fast enough for balance correction to feel stable.

Debugging Hardware

Problems can come from code, wiring, sensor behavior, or mechanical balance, so debugging requires a broader approach.

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Lessons Learned

• Applying software logic to physical systems
• Working with sensor feedback
• Thinking about timing and control loops
• Debugging across hardware and software boundaries

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Future Improvements

• Tune control constants more precisely
• Add telemetry output for debugging
• Improve physical chassis stability
• Add remote control or mobile monitoring
• Document wiring and calibration steps