The objective of this project was to systems engineer a payload attached to a drone to detect target drones and disable them. This project was part of my Systems Engineering course at Toronto Metropolitan University. My team and I designed and showcased a functional prototype for a drone payload. The images below showcase the final design iteration with the supply of power to the ESP32-cam and the Arduino Uno microcontroller connected to the receiver module.
Ground station and payload subsystem interaction
Transmitter and receiver connection modules
Systems architecture for the prototype
Successful detection of target image by the trained YOLOv3 model
The images above describe the ground station, payload, and communications subsystem diagrams, as well as their integration. They illustrate the overall system architecture, highlighting the key components and their interconnections. The ground station diagram showcases the control interface, data processing units, and antenna systems responsible for sending and receiving signals. The payload diagram details the onboard instruments and sensors used for data collection and mission execution. The communications subsystem diagram outlines the signal transmission and reception process, including the frequency bands and range that had to be modified to increase signal strength. These diagrams demonstrate how the subsystems interact to enable seamless data acquisition, transmission, and real-time monitoring.
I collaborated with a team of four individuals, where we divided responsibilities into structures, project management, blueprinting & simulation, system prototyping, ground testing, and documentation & reporting teams. I led the blueprinting, prototyping, and ground testing teams, overseeing the design and development of prototype iterations. My contributions included building the electrical and communication subsystems, as well as designing the connections needed to power and integrate components such as the SG90 servo motor and the 433 MHz transmitter and receiver pair.
Work Breakdown Structure (WBS)
Results
The project successfully met its objective of identifying and disabling a target, fulfilling the stakeholder's design requirements. The payload remained within the initial budget constraint of $50 and adhered to the maximum allowable dimensions of 20 cm × 18 cm × 10 cm. It relayed the target detection to the ground station and deployed the net onto the target. The system's responsiveness demonstrated its capability to perform in real-time scenarios, highlighting its potential for practical applications.
Several design improvements could further enhance the system's performance and efficiency. Reducing the payload's weight by minimizing the chassis profile would optimize the drone's maneuverability and flight endurance. Additionally, reinforcing the internal stability of the payload would be essential when the drone performs evasive maneuvers or operates at high speeds, ensuring the components remain securely in place. Improving the communication subsystem by enhancing signal reliability and reducing latency would strengthen the real-time feedback loop between the payload and the ground station. Lastly, incorporating a more robust power management system could extend the operational duration, increasing the system's overall effectiveness.
The video showcases the testing of the payload design in identifying the target. Since this was a prototype, the target was a set of images instead of a physical drone as it was the minimum requirement to demonstrate the successful identification of a target.