Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 32 | Autonomous Target-Following Quadcopter with Real-Time YOLO Vision and Custom Flight Controller |
Jintu Guo Renang Chen Zhenbo Chen Zhengyu Zhu |
proposal1.pdf |
Lin Qiu | |
| # Problem Small unmanned aerial vehicles (UAVs) are widely used in applications such as aerial filming, search-and-rescue, and surveillance. However, most consumer-grade FPV (First Person View) drones rely entirely on manual control and lack the ability to autonomously track moving targets. We aim to design an autonomous target-following quadcopter system that leverages edge computing for real-time object detection. The drone needs to recognize a specific target using a vision system and autonomously follow it while maintaining stable flight, bridging the gap between manual FPV drones and expensive enterprise autonomous platforms. # Solution Overview Our solution consists of a custom-built 5-inch quadcopter (using a Mark frame, 2207 brushless motors, and 55A ESCs) equipped with an Orange Pi 5 as the central vision processor, and a custom-designed Flight Controller PCB. The Vision Subsystem on the Orange Pi 5 will run a YOLO object detection model to capture and identify the target's relative position. This spatial data is sent to our custom Flight Control Subsystem (STM32-based), which executes a closed-loop PID control algorithm to adjust the drone's attitude and thrust. To meet the high power demands of the vision board, the Power Subsystem—integrated into our custom PCB—will feature an optimized three-level buck converter to safely and efficiently step down the high-voltage LiPo battery to a stable 5V/4A supply. A Remote Control Subsystem will allow FPV manual override and mode switching. # Solution Components ## Power Subsystem A custom PCB integrating an advanced three-level buck converter. It steps down the voltage from a high-capacity LiPo battery (sized appropriately to target a 15-minute flight time) to provide a stable, low-ripple 5V/4A power supply for the Orange Pi 5, while routing raw power to the 55A Electronic Speed Controllers (ESCs). ## Flight Control Subsystem The core of our custom PCB, built around an STM32 microcontroller and an IMU (e.g., MPU6000/BMI270). It receives tracking vectors from the Vision Subsystem and user inputs from the receiver, generating precise PWM signals for the ESCs to stabilize the drone and follow the target. ## Vision Subsystem An Orange Pi 5 paired with a high-framerate camera module. It runs a YOLO-based object detection algorithm to process video feeds in real-time, computing the bounding box and relative spatial coordinates of the target object. ## Remote Control & Interaction Subsystem A wireless FPV radio receiver link that allows the operator to manually control the drone, monitor telemetry, and safely toggle between manual FPV flight mode and autonomous tracking mode. # Criterion for Success - The custom three-level buck converter on the PCB can stably output 5V at 4A under continuous load, and sustain a peak current of 5A for up to 10 minutes without requiring additional active cooling or resetting the Orange Pi 5. - The Vision Subsystem (Orange Pi 5) successfully runs the YOLO model at a minimum of 30 FPS to detect and output the relative coordinates of a target. - The flight controller can smoothly process vision data to autonomously follow a target moving at a walking pace (1-2 m/s), keeping the target within the camera's field of view for at least 15 seconds. - The customized power distribution and selected LiPo battery support a continuous flight/hover time approaching 15 minutes. - The remote control system allows seamless switching between autonomous tracking and manual FPV override with a control latency of less than 300 ms. # Distribution of Work - **Zhenbo Chen (EE):** Power Subsystem design. Responsible for the custom PCB schematic and layout of the high-efficiency three-level buck converter and power distribution to the ESCs. - **Zhengyu Zhu (EE):** Flight Control Subsystem design. Responsible for the STM32 integration on the custom PCB, IMU sensor fusion, and embedded PID flight control firmware. - **Renang Chen (ECE):** Hardware Assembly and Systems Integration. Responsible for the Mark frame mechanical build, 2207 motor/55A ESC integration, battery sizing/testing, and Remote Control Subsystem configuration. - **Jintu Guo (ECE):** Vision Subsystem design and implementation. Responsible for configuring the Orange Pi 5, deploying the YOLO model, and writing the serial communication protocol to send coordinate vectors to the STM32. # Justification of Complexity We believe our project possesses the significant electrical and embedded systems complexity required for ECE 445. The hardware core of this project is a highly complex custom PCB that must integrate a sensitive STM32 flight controller alongside a high-current, high-efficiency three-level buck converter. Delivering a clean 5V/4A to the Orange Pi 5 in an extremely noisy FPV drone environment (caused by 55A ESCs and high-KV 2207 motors) requires rigorous PCB layout, impedance matching, and thermal management skills. Furthermore, developing the embedded C firmware on the STM32 to bridge FPV radio inputs with autonomous YOLO-derived spatial vectors from a Linux board involves advanced knowledge of control theory, sensor fusion, and real-time communication protocols. |
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