Week 1

During the first week of my DREU project, I familiarized myself with the university and the surrounding area. I met with Dr. Crawford and my mentors to discuss the project details, gaining a comprehensive understanding of its requirements. I began exploring the APIs and documentation for Makeblock and CyberPi, which are crucial for my project. This initial research phase laid a strong foundation for the technical work ahead.

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Week 2

In the second week, I started experimenting with various sensors from the Keyestudio Sensor Kit, focusing on data collection methods. I created multiple programs to detect touch, movement, sound, and voltage. Additionally, I developed a way to connect CyberPi to Wi-Fi for data collection. I also programmed line plots and box plots to visualize the collected data.

Here are a few examples of the programs I created:

  • Touch Sensor Program: Detected and displayed touch inputs.
  • Movement Sensor Program: Captured motion data.
  • Sound Sensor Program: Measured and visualized sound levels.
  • Voltage Sensor Program: Monitored voltage changes.
  • Wi-Fi Connectivity: Enabled data collection over the network and visualized it using line and box plots.

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Week 3: Enhancing Data Visualization Techniques

This week, I worked on refining the data visualization aspect of the project. I explored more advanced plotting techniques such as time-series line plots for motion and voltage data, and bar graphs for comparing sound levels over time. This provided a clearer picture of sensor readings and their trends. I also tested the Wi-Fi streaming capabilities, ensuring smooth data transmission from sensors to the visual interface.

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Week 4: Integrating Multiple Sensors

I began integrating multiple sensors simultaneously to capture more complex data. I connected the touch, motion, and sound sensors to work in tandem, collecting data in real-time. I adjusted the scripts to handle data from multiple sensors and display it through dynamic plots. This enabled me to monitor environmental changes using various parameters at once, increasing the potential applications of the system.

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Week 5: Data Collection and Analysis Refinements

During week 5, I focused on refining the accuracy of the data collection. I adjusted the calibration of each sensor to improve precision and reliability in the real-time data streams. Additionally, I started analyzing the collected data more rigorously, adding basic statistical summaries like means and standard deviations to better understand trends and anomalies.

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Week 6: Introducing More Sensors

In week 6, I added new sensors, such as temperature and humidity, to further expand the system’s environmental monitoring capabilities. This addition allowed me to create real-time visualizations that showed the interaction between environmental factors like temperature and humidity. I also worked on scaling the data collection scripts to handle larger amounts of data as more sensors were integrated.

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Week 7: Transition to Drone Control Project

This week marked the beginning of my transition towards the drone control project. I revisited my earlier research on Neuroblock and how it can be integrated with a BCI headset to control a Tello drone. I spent time understanding the interface between the Tello drone and Neuroblock, preparing for the next stage where I would combine sensor data with drone control.

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Week 8: Implementing Electron for Neuroblock Integration

In the eighth week, I shifted focus to Electron to set up the environment for Neuroblock on my Mac. I had to work through some initial compatibility issues to ensure Electron could run smoothly. Once set up, I started developing the Electron interface that would allow me to integrate the BCI headset, Neuroblock, and the Tello drone. This setup was crucial for allowing real-time control of the drone using brainwave data.

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Week 9: BCI and Drone Testing

With Electron and Neuroblock running, I tested the BCI headset’s connection to the Tello drone. I successfully mapped basic brainwave signals (e.g., focus and relaxation levels) to the drone’s flight commands, such as takeoff, landing, and directional control. This was a challenging but rewarding process, and the initial results showed potential for real-time control using BCI signals.

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Week 10: Final Integration and Presentation

In the final week, I focused on fully integrating the drone control with real-time sensor data. I set up a system where data from the Keyestudio sensors could alter the drone’s flight patterns based on environmental readings. For example, high temperature or motion could trigger specific drone maneuvers. I prepared a final presentation demonstrating the complete project, showing how the integration of physical sensors, real-time data, and brainwave-controlled drone flight can work in harmony.

Written on June 1, 2020