Pros of PX4-Autopilot

• More modular architecture, making it easier to add new features or modify existing ones
• Better support for newer hardware and sensors
• More active development community with frequent updates

Cons of PX4-Autopilot

• Steeper learning curve for beginners
• Less extensive documentation compared to ArduPilot
• Fewer supported vehicle types (mainly focused on multicopters and fixed-wing aircraft)

Code Comparison

PX4-Autopilot (C++):

void MulticopterPositionControl::control_position(const float dt)
{
    // Position control logic
    Vector3f pos_error = _pos_sp - _pos;
    Vector3f vel_sp = pos_error * _params.pos_p;
    control_velocity(dt, vel_sp);
}

ArduPilot (C++):

void AC_PosControl::pos_to_rate_xy(float dt, float ekfNavVelGainScaler)
{
    // Position control logic
    Vector3f pos_error = _pos_target - _inav.get_position();
    _vel_target.xy() = pos_error.xy() * _p_pos_xy.kp();
    _vel_target.xy() *= ekfNavVelGainScaler;
}

Both projects use similar approaches for position control, but PX4-Autopilot's code tends to be more modular and object-oriented, while ArduPilot's code is more procedural and tightly integrated with its overall system architecture.

Pros of Betaflight

• Lightweight and optimized for racing drones and FPV flying
• Faster loop times and lower latency for more responsive control
• Simpler setup and configuration process for beginners

Cons of Betaflight

• Limited support for larger, more complex drone configurations
• Fewer advanced features for autonomous flight and mission planning
• Less extensive documentation and community support compared to ArduPilot

Code Comparison

ArduPilot (example of parameter handling):

AP_Int8 dummy_var;
const AP_Param::GroupInfo var_info[] = {
    // @Param: DUMMY_VAR
    // @DisplayName: Dummy Variable
    // @Description: A dummy variable for demonstration
    // @Range: 0 100
    // @User: Standard
    AP_GROUPINFO("DUMMY_VAR", 0, ParameterExample, dummy_var, 0),
    AP_GROUPEND
};

Betaflight (example of PID controller implementation):

void pidController(const pidProfile_t *pidProfile, timeUs_t currentTimeUs)
{
    // PID controller code
    for (int axis = FD_ROLL; axis <= FD_YAW; axis++) {
        // Calculate PID terms
        // Apply PID output
    }
}

Pros of iNav

• Lighter and more efficient codebase, suitable for smaller drones and multicopters
• Better support for fixed-wing aircraft and transitional VTOL platforms
• More user-friendly configuration process through a graphical interface

Cons of iNav

• Less extensive feature set compared to ArduPilot
• Smaller community and ecosystem, resulting in fewer third-party integrations
• Limited support for larger vehicles and professional/industrial applications

Code Comparison

ArduPilot (example of parameter handling):

AP_Int8 format_version;
AP_Int8 software_type;

const AP_Param::Info var_info[] = {
    // @Param: FORMAT_VERSION
    // @DisplayName: Eeprom format version number
    // @Description: This value is incremented when changes are made to the eeprom format
    // @User: Advanced
    AP_GROUPINFO("FORMAT_VERSION", 0, AP_Vehicle, format_version, 0),
    // ...
};

iNav (example of parameter handling):

typedef struct {
    const char *name;
    const uint16_t type; // type of variable
    const uint8_t flags;
    void *ptr; // pointer to variable in memory
    const int32_t min;
    const int32_t max;
} __attribute__((packed)) setting_t;

Both projects use different approaches to parameter handling, with ArduPilot using a more complex system that includes additional metadata, while iNav opts for a simpler structure.

Pros of Cleanflight

• Lightweight and optimized for racing drones and small quadcopters
• User-friendly configuration interface with Cleanflight Configurator
• Faster loop times and lower latency for improved flight performance

Cons of Cleanflight

• Limited support for larger drones and non-quadcopter configurations
• Fewer advanced features compared to ArduPilot (e.g., mission planning, obstacle avoidance)
• Smaller community and less extensive documentation

Code Comparison

ArduPilot (example of parameter handling):

AP_Int8 dummy_var;
const AP_Param::GroupInfo var_info[] = {
    // @Param: DUMMY_VAR
    // @DisplayName: Dummy Variable
    // @Description: This is a dummy variable
    // @Range: 0 10
    // @User: Standard
    AP_GROUPINFO("DUMMY_VAR", 0, ParameterExample, dummy_var, 0),
    AP_GROUPEND
};

Cleanflight (example of PID controller implementation):

void pidController(const pidProfile_t *pidProfile, const rollAndPitchTrims_t *angleTrim, timeUs_t currentTimeUs)
{
    static timeUs_t previousTimeUs;
    const float dT = (currentTimeUs - previousTimeUs) * 1e-6f;
    previousTimeUs = currentTimeUs;

    // ... (PID calculation logic)
}

Pros of Paparazzi

• More lightweight and modular architecture
• Easier to customize and extend for specific research needs
• Better support for fixed-wing aircraft and unconventional designs

Cons of Paparazzi

• Smaller community and less commercial support
• Fewer supported hardware platforms
• Steeper learning curve for beginners

Code Comparison

Paparazzi (main flight control loop):

void autopilot_periodic(void) {
  if (autopilot_in_flight) {
    nav_periodic_task();
    guidance_h_run(autopilot_guidance_h_mode);
    guidance_v_run(autopilot_guidance_v_mode);
  }
}

ArduPilot (main flight control loop):

void Copter::fast_loop()
{
    // IMU update
    ins.update();
    // run low level rate controllers that only require IMU data
    attitude_control->rate_controller_run();
    // send outputs to the motors library
    motors_output();
}

Both projects implement similar flight control loops, but Paparazzi's approach is more modular and easier to customize, while ArduPilot's implementation is more tightly integrated and optimized for performance.