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RoboticsStatus: CompletedYear: 2026

Robotics Telemetry Processor

Embedded C++ sensor data pipeline filtering noisy MPU-6050 accelerometer and gyroscope signals for robot navigation.

01. Overview & Description

Developed during my Robotics Internship at IEEE Sensors Council & Luminar Technolab to stabilize mobile robot tilt calculations.

02. System Architecture Diagram

MPU-6050 IMU Sensor -> I2C Bus -> C++ Complementary Filter -> Serial Stream -> Python Plotter

Interactive System Flow Diagram

Click any node below to inspect its operational role in the pipeline.

● Live Animated Flow
1React18 DashboardFrontend Client2FastAPIEngineREST & SSE Middl3Authentication/ RLSSecurity Policy4PostgreSQL/ SQLitePersistence Laye5LocalOllama LLMQuantized Infere6StreamedResponseUI Rendering Out
Selected Node: React 18 Dashboard(Frontend Client)

Handles user prompt input, interval charts, SSE streaming data rendering, and dark-theme state management.

03. Database Schema & API Specifications

Database Schema Design

Circular memory buffer on microcontroller holding 200 telemetry frames for real-time moving average calculation.

API Endpoints
  • SensorPipeline::init()
  • SensorPipeline::update(float dt)
  • SensorPipeline::getOrientation()

04. Codebase Folder Structure

robotics-telemetry/
├── src/
│   ├── ComplementaryFilter.cpp
│   ├── IMUDriver.cpp
│   └── main.cpp
├── python_plotter/telemetry_plot.py
└── README.md

05. Visual Evidence & Artifacts

Live Orientation Waveformterminal

$ robotics-telemetry-processor --status active

[OK] System pipeline initialized

Filtered tilt estimation (blue) vs raw noisy accelerometer readings (red)

06. Engineering Challenges

Eliminating cumulative gyroscopic drift over long operating intervals without incurring heavy floating-point calculation delay on 16MHz CPUs.

07. Key Lessons Learned

Filtering noise at the sensor intake layer prevents exponential error accumulation in downstream navigation algorithms.

Technologies Used

RoboticsC++ArduinoSensorsEmbedded SystemsI2C