Drone Development

Hardware Description

500 PCB 500mm F450 Quadcopter Frame Kit w/Landing Gear MINI S800 EVO Design

Features

• - F450 upgrade quadcopter

• - Learned arm design from DJI S800 EVO, and enhanced

• - good work with Gopro Hero 3 2 Axis brushless gimbal for FPV

• - PCB Center plate with solder point

• - high quality carbon fiber landing gear

• - Frame only, no electronic parts.

Specifications

• - Wheelbase: 500mm

• - Motor suggest: 2212 KV980; 2216 KV880 KV900; 3108 900KV

• - Prop suggest: 1045/1047/1147/1238

• - Battery suggest: 3S-4S 2200mAh-5200mAh

Pixhawk PX4 Autopilot PIX 2.4.8 32 Bit Flight Controller

Features

1. The advanced 32-bit ARM CortexM4 high-performance processors, can run NuttX RTOS real-time operating system;

2. 14 PWM / servo output;

3. Bus interface (UART, I2C, SPI, CAN);

4. The integrated backup power and backup controller fails, the primary controller fails over to the backup control is safe;

5. Provide automatic and manual modes;

6. Provide redundant power input and failover;

7. Multicolor LED lights;

8. Provide multi-tone buzzer Interface

9. Micro SD recording flight data;

Specifications

Microprocessor

• 1.32 2 MB flash STM32F427 Cortex M4, with hardware floating-point processing unit

• 2. frequency: 168MHZ, 256K RAM

• 3.32 STM32F103 backup coprocessor

Sensor

• 1.L3GD20 3-axis digital gyroscope 16

• 2.LSM303D 3-axis accelerometer 14 / Magnetometer

• 3.MPU6000 6-axis accelerometer / magnetometer

• 4.MS5611 precision barometer

Interface

1. 5 high voltage compatible UART 1, 2 with hardware flow control

2. 2 CAN

3. Spektrum DSM / DSM2 / DSM-X satellite receiver compatible input

4. Futaba SBUS compatible inputs and outputs

5. PPM Signal Input

6. RSSI (PWM or voltage) Input

7. I2C

8. SPI

9. 3.3 and 6.6VADC input

10. External MICRO USB Interface

Scope

This project aims to design and develop a modular drone platform using a 500mm F450 Quadcopter Frame Kit and the Pixhawk PX4 Autopilot. The core objective is to integrate AI-based control models to enhance drone capabilities for diverse autonomous tasks. Key functionalities will include executing predefined aerial maneuvers, such as performing flips, and utilizing a camera-based vision system to recognize hand gestures, faces, and objects for dynamic interaction and response.

The project will also explore real-time decision-making, advanced stabilization techniques, and optimized flight paths. Emphasis will be placed on scalability, allowing future expansion with additional AI models, improved hardware integration, and advanced control algorithms. This system is envisioned to serve as a foundation for experimental AI-driven drone services and applications in automation, inspection, and interactive technology demonstrations.