A description of this project will be posted soon. In the mean time, you can view the work in progress photos below.

Starlink pan-tilt mount and satellite tracker

The purpose of this project was to empower novice programmers to use Python for analyzing data from microcontroller sensors placed in physical environments. It uses data collected from small LIDAR modules to generate point cloud data visualizations.

Microcontrollers, like those produced by Arduino and its derivatives, are frequently used by researchers and hobbyists to build custom sensor tools and applications. However, software for these devices tends to be written in the C++ programming language, which may be a barrier to use for non-professional software engineers.

Code for this project is open source and available here: 

https://github.com/jamescoledesign/lidar-py

 during the Fall 2023 semester at UTD. It was rated as the class favorite among all 24 final projects in the class.

Emerging implementations of Python for microcontrollers, such as MicroPython [4] and CircuitPython [5], highlight an increasing interest in using high-level programming languages to control these devices. Since Python is the most widely used programming language for people who are not professional software developers [6], exposing data collected from microcontrollers via Python may expand access to low-cost hardware prototyping tools.

Connect microcontroller components to power and control a LIDAR module (3.3 V) and servo motors (5 V)

Program microcontroller to scan the surrounding environment 

Get LIDAR distance measurement (cm), servo pitch (degrees), and servo yaw (degrees)

Convert distance, pitch, and yaw from spherical coordinates to Cartesian coordinates

Print spherical coordinates to serial monitor (via USB)

Read serial monitor in Python application

Create DataFrame of spherical coordinates

Convert spherical coordinates to point cloud

The Python program was able to access the serial port to collect LIDAR distance measurements from the Arduino prototype.

Data was successfully converted into a point cloud and processed using PyVista, opening the possibility for robust spatial data streaming, analysis, and visualization from microcontroller sensors in Python applications.

To address some the power limitations imposed by two servos, I moved the LIDAR module to a dedicated microcontroller and added a large capacitor to the servo circuit. I then modified my Python program to monitor two serial ports simultaneously, combining the data from each serial port into a single pandas DataFrame. These modifications dramatically increased the speed at which points can be collected.

Different angles (brightness enhanced for web)

For this version, I used a RPLidar A1 attached to a single servo. The Python program was able to collect and plot data from this device in much the same way by incorporating the 

. This device can collect many more points in a short amount of time than the Garmin device, but the arc of the servo and the formula for converting Cartesian coordinates to spherical coordinates still need to be adjusted to generate accurate point cloud visualizations.  

While Python libraries to analyze and visualize point cloud data are relatively straightforward to implement, the complexities of setting up microcontrollers with custom sensor configurations may present significant challenges for novice programmers. Nevertheless, this prototype successfully captured LIDAR data, passed the data to a Python application, and generated a point cloud. This demonstrates the possibility of integrating low-cost sensor components with open source Python libraries for data collection and analysis.

Despite the relative successes of this project, the LIDAR module initially used was limited by measurements taken in one direction. Generating point clouds that capture 3D aspects of an environment required the addition of servos to pan and tilt the LIDAR module. Adding more electrical components increased power load and reduced the efficiency of this analysis pipeline. Troubleshooting these types of problems might be a frustrating roadblock for a novice. Future work should explore alternative sensors, ways to visualize power draw on the system, and propose alternative tools and methods for generating point clouds.

Streaming data from sensors directly into Python applications may enable rapid creation of datasets to be used for machine learning (ML). Sensor data could then be interpreted using ML tools on a connected peripheral or via ML tools optimized for microcontrollers (e.g., TensorFlow Lite for Microcontrollers)[8]. Future work could allow users to label sensor data so that is useful for training ML models.

Data collected using the Garmin LIDAR-Lite v4 (Qwiic) module from SparkFun

Example point cloud data from PyVista [2,3]

Apparatus design inspired by Charles Grassin [1] and Travis Ledo [7]. Code for spherical coordinate conversion was copied from Charles Grassin’s open source project.

Charles Grassin. 3D Lidar Scanner. Charles’ Labs. Retrieved November 14, 2023 from https://charleslabs.fr/en/project-3D+Lidar+Scanner

PyVista. 2023. Create Point Cloud. PyVista Documentation. Retrieved November 14, 2023 from https://docs.pyvista.org/version/stable/examples/00-load/create-point- cloud.html#sphx-glr-examples-00-load-create-point-cloud-py

C. Sullivan and Alexander Kaszynski. 2019. PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). JOSS 4, 37 (May 2019), 1450. https://doi.org/10.21105/joss.01450 

MicroPython - Python for microcontrollers. Retrieved November 14, 2023 from http://micropython.org/ 

CircuitPython. Retrieved November 14, 2023 from https://circuitpython.org/ 

Stack Overflow Developer Survey 2023. Stack Overflow. Retrieved November 14, 2023 from https://survey.stackoverflow.co/2023/?utm_source=social- share&utm_medium=social&utm_campaign=dev-survey-2023 

Arduino Lidar Scanning & Java Rendering. projecthub.arduino.cc. Retrieved November 14, 2023 from https://projecthub.arduino.cc/TravisLedo/arduino-lidar-scanning-java-rendering-801369 

TensorFlow Lite for Microcontrollers. TensorFlow. Retrieved November 14, 2023 from https://www.tensorflow.org/lite/microcontrollers

LIDAR Scanner and Data Visualization in Python

In 2023, I tracked temperature fluctuations during the annular eclipse using an Acurite Atlas weather station, showing a sight temperature drop corresponding with decreased light intensity during the max of the eclipse.  

During the 2024 total eclipse, I captured the same measurements using both AcuRite Atlas from the prior year and a microcontroller fixed with a temperature sensor and ambient light sensor. I was also interested to see if temperature is impacted to a greater extent during a total eclipse versus an annular eclipse. 

Predictably, the temperature dropped during the eclipse and rose shortly after the eclipse max. The microcontroller setup showed more variation in measurements than the Acurite Atlas, but recorded a similar relationship between ambient light and temperature during the eclipse.


I also compared measurements between the 2023 annular eclipse and 2024 total eclipse. Interestingly, the temperature only fell ~0.3 degrees lower between the start and max of the total eclipse than it did during the same interval of the annular eclipse.


I used the data to create an animated visualization of changes in light intensity during the eclipse. In Adobe After Effects, the LUX values at each recorded interval modify the opacity of a stock image of the sun. A black circle moves across the sun to indicate the approximate position of the moon at that moment during the eclipse. During the eclipse max, the moon is centered over the sun image and the LUX drops to a value of 1.

Solar Eclipse Temperature And Light Intensity Measurement

Introduction

Analysis

Visualization

Starlink pan-tilt mount and satellite tracker

LIDAR Scanner and Data Visualization in Python