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119 lines
6.1 KiB
Markdown
119 lines
6.1 KiB
Markdown
# Arduino-Photometrics <!-- ![CI][] -->
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**Arduino-photometrics** is sub-module of **Robot Go West** project.
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The goal of this project is to collect local lighting data to train a prediction model capable of estimating the sun's position.
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The system is structured as follows:
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* **Embedded Layer**: Developed in **C++** for real-time data collection.
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* **Data Management**: Python scripts are used to update the RTC (Real-Time Clock) and extract measurement data from the device files.
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## Arduino Mounting
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## EEPROM Header and Data Storage
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The system collects data and writes it to the EEPROM during deployment.
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To better understand the storage structure within the EEPROM, please refer to the diagram below.
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## Executable Files
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All executable files are located in the `exec/` folder.
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## Tutorial
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### 1. Hardware Setup
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* **Assembly**: First, assemble the Arduino (refer to the diagram above).
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* **3D Printing (Optional)**: You can download and print the support from [PotPhotoResistance](https://git.cohabit.fr/gowest/gowest/src/branch/photosupport/photosupport/), designed by **Jhodi Avizara**.
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### 2. Software Configuration
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* Download or clone the project from this repository.
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* Open the project directory in **VSCode** with the **PlatformIO** extension.
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* Ensure PlatformIO has successfully initialized the project.
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### 3. Testing & Deployment
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* Connect the Arduino to your PC.
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* Run the project tests using the GUI or the command `pio test`.
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* Upload the code to the Arduino.
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* Perform the [RTC update](#rtc-update) and the [Sensor calibration](#sensor-calibration).
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* **Important**: Ensure the `serial_com` flag is set to `false` (this boolean flag manages the switch between measurement and communication phases).
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### 4. Data Collection
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* Place the Arduino near a window and let it collect data.
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* After a sufficient period, reconnect the Arduino to your PC.
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* Retrieve the data using the [Gather measures](#gather-measures) script.
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### Sensor calibration
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The goal of this step is to determine the measurement range by identifying the lower and upper thresholds of your sensors.
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1. **Setup**: After completing the electrical assembly, call the `print_min_max_res()` method from the **SensorManager** object within the main loop (then compile and upload to the Arduino).
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2. **Calibration**:
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* **Dark state**: Place the light sensors in a dark environment (e.g., inside a light-proof box) to record the minimum values.
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* **Bright state**: Expose the sensors to direct sunlight or a bright lamp to record the maximum values.
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3. **Configuration**: Once you have the values from the Serial monitor, update the `min_res` and `max_res` variables for each sensor in your code.
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The software will automatically normalize these values to fit into a **uint8_t** type. This compression minimizes the data size, allowing for more efficient storage in the EEPROM.
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### RTC Update
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1. **Enable Debug Mode**: Before building the project, uncomment the `-D DEBUG` line in `platformio.ini`.
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2. **Set Phase**: Ensure the device is in the [Communication Phase](#set-communication-phase).
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3. **Monitor Serial Port**: Check the serial output to verify the current date and time.
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4. **Sync Time**: If the date is incorrect, run the `time.py` script located in the `exec/` folder:
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```bash
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python3 exec/time.py
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```
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The Python script synchronizes the Arduino's RTC with the computer's system clock via Serial communication.
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### Gather Measures
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After the data collection period is complete:
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1. **Switch Mode**: Set the device to the [Communication Phase](#set-communication-phase).
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2. **Download Data**: Run the `download_csv.py` script to retrieve your measurements and generate a CSV file:
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```bash
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python3 exec/download_csv.py
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```
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**Important**: The download process will **wipe the entire Arduino EEPROM memory** to prepare it for the next collection cycle.
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### Set Communication Phase
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1. Connect the Arduino to your PC.
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2. Set the `serial_com` boolean flag to `true` in the code.
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3. **Warning**: To avoid data corruption, ensure the system is not actively writing measurements to the EEPROM before uploading the new configuration.
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## Known Limitations & Future Improvements
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### Data Structure
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* **Current Issue**: The storage system is optimized for `uint8_t` data types.
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* **Limitation**: Generic template functions for other data types are currently non-functional. Implementing a flexible template system is planned for future versions.
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### Power Management
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* **Current State**: The system is designed for a continuous power supply (standard electrical outlet).
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* **Improvement**: Future hardware and software iterations will focus on battery-powered operation and low-power sleep modes to enable true portability.
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### CI/CD Pipeline (GitLab)
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* **Current Issue**: The automated pipeline currently fails during virtual hardware testing.
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* **Challenge**: Need to configure serial port interfacing between the Virtual Machine (VM) and the pipeline runner to enable remote hardware-in-the-loop testing.
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### Calibration Process
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* **Improvement**: Develop an assisted calibration tool to automatically capture and store threshold values, replacing the current manual code update process.
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[CI]: https://gitlab.com/Luci_/arduino-photometrics/badges/main/pipeline.svg
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# Prediction Model
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The system is designed to monitor solar irradiance from a fixed position (e.g, my system is positioned behind a window with a **109° East-of-North** orientation).
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The goal is to map the internal lighting patterns to the sun's actual coordinates, allowing the model to estimate the sun's position based solely on photometer data.
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# Credits
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This project was entirely developed by **Aurélien Gauthier**.
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The 3D model for the sensor support was designed by **Jhodi Avizara**.
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### Dependencies & Libraries
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This project utilizes the following libraries:
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* [Low-Power](https://registry.platformio.org/libraries/rocketscream/Low-Power): Used for power management and system sleep cycles.
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* [DS3231 Lib](https://registry.platformio.org/libraries/northernwidget/DS3231): Used for interfacing with the DS3231 RTC module. |