A Low-Cost Platform for Measuring Backlash in Popular UART Servos

This repository contains the source code and data analysis scripts for the research paper **"A Low-Cost Platform for Measuring Backlash in Popular UART Servos"**. The project presents an open-source test stand for characterizing backlash in low-cost UART servo actuators (Feetech, Waveshare, etc.) using controlled force application and telemetry logging. --- 📂 Project Structure ```text ├── cad/ # Hardware design files (.step and .stl) ├── logs/ # Output directory for telemetry data (CSV) ├── software/ │ ├── backlash_test/ │ │ ├── .env # Environment variables │ │ ├── .gitignore # Git ignore rules │ │ ├── app.js # Main Node.js controller application │ │ ├── config.js # Serial port configuration │ │ ├── package.json # Node.js dependencies and scripts │ │ ├── sweepConfig.js # Configuration for motion sequences │ │ └── TelemetryLogger.js # Module for CSV data logging │ ├── logs_analysis/ │ │ ├── config/ # Example config files │ │ ├── log_calc.py # Calculates backlash statistics based on provided CSV log file │ | ├── log_viz.py # Generates visualisation from provided CSV log file │ | ├── config_utils.py # Some common utilities used by analysis and visualisation scripts ``` 🛠 Hardware Requirements To reproduce the experiments, you will need: Servos: 2x Feetech STS3215 (puller motors) + 1-2 servos for device under test. Controller: USB-TTL Adapter (e.g., Feetech or Waveshare USB-TTL) connected to a PC. Power Supply: 7.4V or 12V DC power supply (2A minimum). Mechanical Assembly: 3D-printed servo holders and 100mm test levers (see paper for details). 🚀 Getting Started 1. Controller Setup (Node.js) The Node.js application manages the servos, executes motion profiles, and logs telemetry. Prerequisites: Node.js (v18+), NPM. ```bash cd software/backlash_test # Install dependencies npm install # Configure environment variables # Create a .env file or set these manually export SERIAL_PORT=COM3 # Or /dev/ttyUSB0 on Linux/Mac export SERIAL_BAUD_RATE=1000000 ``` Running the Experiment: To start the control loop and logging: ```bash node app.js ``` Configuration for motion patterns and servo IDs is located in `sweepConfig.js`. Logs are automatically saved to the `logs/` directory. 2. Motor analysis utilities Prerequisites: Python 3.8+, numpy, pandas, plotly. `log_calc.py` handles motor-analysis scenario by reading a JSON configuration that describes which motors define the analysis phase window, which motors should be reported, and the stretched/relaxed targets for the actuated motors. Run the script by passing the config and the log CSV: ```bash python logs_analysis/log_calc.py logs_analysis/config/config_m1_m2.json /path/to/log.csv ``` `log_viz.py` uses the exact same config files to decide which motors to plot (from `report_motor_ids`) and which actuators provide the relaxed/stretched overlays. Invoke it the same way: ```bash python logs_analysis/log_viz.py logs_analysis/config/config_m1_m2.json /path/to/log.csv ``` 📄 Research Paper The full text of the research paper is located in the `paper/` directory. You can compile it using any standard LaTeX distribution (TeX Live, MiKTeX) or online editor (Overleaf). 👥 Author Information Boris Kotov Senior Software Engineer, Robotics Control Systems. Email: [boris.k@robonine.com](mailto:boris.k@robonine.com) Company: Robonine Developing accessible robotic solutions for education and research. Website: [robonine.com](https://robonine.com) 📜 License Hardware: Creative Commons Attribution 4.0 International (CC BY 4.0) Software: GNU GPL v3.0 Documentation: Creative Commons Attribution 4.0 International (CC BY 4.0) You are free to share and adapt the material as long as appropriate credit is given.