Drone with a Mind of Its Own

Because Sometimes You Just Need a Flying AI Robot

Building a Groundbreaking Autonomous Drone System

This project involves the integration of multiple cutting-edge components to create an autonomous drone capable of interpreting voice/hand signals and responding accordingly. Below is a beginning outline of how to design and build this system. (work in progress … )

AI Concept

In this detailed guide, we’ll walk you through building an autonomous drone system that integrates a Google Coral TPU, a Raspberry Pi, a SpeedyBee F405 Mini flight controller, and MAVLink protocol for communication. The drone will be controlled via voice commands processed by a large language model (LLM) running on an Android/iOS application. This system leverages Rust on the Raspberry Pi for robust and efficient control, making it both powerful and versatile for various applications.

System Overview

The system consists of the following key components:

1. Google Coral TPU: Provides real-time AI capabilities, such as object detection and image processing.
2. Raspberry Pi: Acts as the central processing unit, running Rust-based applications to manage MAVLink communication and control the drone.
3. SpeedyBee F405 Mini: The flight controller responsible for stabilizing the drone and executing flight commands.
4. BZGNSS BZ-181 GPS Module: Provides accurate positioning data for the drone. The GPS module allows the drone to navigate to specific coordinates, enabling features like autonomous waypoint navigation, return-to-home, and precise positioning near the user.
5. SoloGood Ant Nano Camera: Connected to the Coral TPU, the camera is used for real-time object detection and collision avoidance. The camera captures visual data, which is processed by the Coral TPU to identify obstacles and adjust the drone’s path accordingly.

Mind & Eyes

1. MAVLink: A communication protocol used to send commands to the flight controller and retrieve telemetry data.
2. Android/iOS Mobile Application: Runs the LLM (Gemini), which interprets voice commands and sends corresponding instructions to the drone.

Hardware Setup

How to solder

• make sure both connections are hot — 2/4 secs.
• add solder
• pull the iron way when done\

PINECIL - Smart Mini Portable Soldering Iron (Version 2) - PINE STORE

Wiring the SpeedyBee F405 Mini to the Raspberry Pi

Required Connections

To connect the Raspberry Pi to the SpeedyBee F405 Mini flight controller, you’ll primarily use the UART (Universal Asynchronous Receiver-Transmitter) ports for serial communication, along with power and ground connections.

• TX3 (Transmit) on F405 Mini → RXD (GPIO 15) on Raspberry Pi
• RX3 (Receive) on F405 Mini → TXD (GPIO 14) on Raspberry Pi
• 5V on F405 Mini → 5V Pin (Pin 2 or 4) on Raspberry Pi
• GND on F405 Mini → GND Pin (Pin 6 or any other GND) on Raspberry Pi

Wiring Diagram

Refer to the wiring diagram below to ensure all connections are correctly made.

• Step 1: Locate the UART3 pads on the SpeedyBee F405 Mini, labeled as TX3 and RX3.
• Step 2: Connect the TX3 pad on the F405 Mini to the RXD (GPIO 15) pin on the Raspberry Pi.
• Step 3: Connect the RX3 pad on the F405 Mini to the TXD (GPIO 14) pin on the Raspberry Pi.
• Step 4: Connect a 5V pad on the F405 Mini to the 5V pin on the Raspberry Pi.
• Step 5: Connect a GND pad on the F405 Mini to any GND pin on the Raspberry Pi.

Integrating the BZGNSS BZ-181 GPS Module with the SpeedyBee F405 Mini and Raspberry Pi

Integrating the BZGNSS BZ-181 GPS module into your drone system will provide accurate positioning data, enabling advanced features like autonomous waypoint navigation, return-to-home, and geo-fencing. This section details the steps for wiring, configuring, and integrating the BZ-181 GPS module with the SpeedyBee F405 Mini flight controller and the Raspberry Pi.

Wiring the BZGNSS BZ-181 GPS Module

The BZGNSS BZ-181 GPS module typically communicates with the flight controller via a UART interface. Here’s how you can wire it up:

Body

Required Connections:

• TX (Transmit) on GPS Module → RX (Receive) on SpeedyBee F405 Mini
• RX (Receive) on GPS Module → TX (Transmit) on SpeedyBee F405 Mini
• 5V Power (VCC) on GPS Module → 5V Power on SpeedyBee F405 Mini
• GND on GPS Module → GND on SpeedyBee F405 Mini

Wiring Diagram:

• TX (Transmit) on the GPS Module should be connected to the RX (Receive) pad on UART1 or another available UART on the SpeedyBee F405 Mini.
• RX (Receive) on the GPS Module should be connected to the TX (Transmit) pad on UART1 or the corresponding UART.
• 5V Power (VCC) on the GPS Module should be connected to a 5V pad on the SpeedyBee F405 Mini.
• GND on the GPS Module should be connected to a GND pad on the SpeedyBee F405 Mini.

Configuring the SpeedyBee F405 Mini for GPS

After wiring the GPS module to the flight controller, you need to configure the flight controller firmware (such as Betaflight or INAV) to recognize and utilize the GPS data.

Configuring Betaflight/INAV

Connect to the Flight Controller

• Use Betaflight Configurator or INAV Configurator on your computer.
• Connect the SpeedyBee F405 Mini to your computer via USB.

Enable the GPS Feature

• Navigate to the “Configuration” tab in the configurator.
• Find the “GPS” section and enable the GPS feature by toggling it on.

Set the GPS Protocol

• Select the correct protocol used by the BZGNSS BZ-181 GPS module, typically UBlox for most modules.
• Set the baud rate to 9600 or 115200, depending on the module’s configuration. The default is usually 9600.

Assign the UART Port

• Go to the “Ports” tab in the configurator.
• Enable “GPS” on the UART port where you connected the GPS module (e.g., UART1).
• Save and reboot the flight controller.

Verify GPS Data

• After rebooting, check the “GPS” tab in the configurator to ensure the GPS module is receiving satellite data. You should see values for latitude, longitude, and number of satellites.

Advanced Settings

• Home Point Settings: Configure the home point settings to ensure that the drone correctly identifies its takeoff location, which is crucial for return-to-home functionality.
• Failsafe Settings: Set up failsafe settings to ensure the drone returns home or lands safely if the GPS signal is lost or if the connection to the controller is lost.

Integrating GPS with the Raspberry Pi and MAVLink

To fully utilize the GPS data within your autonomous drone system, you need to ensure that the GPS data is available to the Raspberry Pi and can be used within the MAVLink framework.

GitHub - mavlink/rust-mavlink: MAVLink library for Rust.

MAVLink and GPS Data:

• Receiving GPS Data: The SpeedyBee F405 Mini sends GPS data as part of the MAVLink telemetry stream. You can access this data on the Raspberry Pi via the MAVLink messages.
• Example Code to Access GPS Data in Rust:

use mavlink::{MavConnection, common::MavMessage};

fn main() {
    let mut conn = MavConnection::open("/dev/serial0").unwrap();
    while let Ok((msg, _)) = conn.recv() {
        if let MavMessage::GPS_RAW_INT(gps) = msg {
            println!(
                "Lat: {}, Lon: {}, Alt: {}, Satellites: {}",
                gps.lat, gps.lon, gps.alt, gps.satellites_visible
            );
        }
    }
}

Utilizing GPS Data:

• Waypoint Navigation: Use the GPS data to implement waypoint-based navigation. You can set a series of GPS coordinates that the drone will follow autonomously.
• Return-to-Home Functionality: Implement return-to-home by storing the takeoff GPS coordinates and navigating back to them when needed.

Sending GPS-Related Commands:

• Implement commands in your Android application to interact with the GPS data. For example, you can create commands like “Fly to [latitude, longitude]” or “Return to home,” which will be interpreted by the Raspberry Pi and sent to the flight controller via MAVLink.

Testing the GPS Integration

Initial Ground Testing:

• Power on the drone in an open area with a clear view of the sky.
• Verify that the GPS module locks onto satellites and provides accurate location data to the flight controller and the Raspberry Pi.
• Check the GPS data in the Betaflight/INAV configurator to ensure it’s being correctly interpreted.

Flight Testing:

• Perform a test flight to ensure that the GPS integration works as expected.
• Test features like return-to-home and waypoint navigation to verify that the GPS data is being utilized correctly.

Troubleshooting:

• If the GPS module does not lock onto satellites or the data seems incorrect, check the wiring and configuration settings in Betaflight/INAV.
• Ensure the GPS module has a clear view of the sky and is not obstructed by any parts of the drone.

Integrating the BZGNSS BZ-181 GPS module into your drone system provides critical navigation capabilities that enhance autonomous operation. By following these steps, you can wire, configure, and utilize the GPS data to implement advanced features like waypoint navigation, return-to-home, and geo-fencing, all controlled via your Android application and the MAVLink protocol on the Raspberry Pi.

Power Supply Overview

Proper power management is crucial to ensure that all components of the drone, including the flight controller, Raspberry Pi, Coral TPU, camera, and any other peripherals, receive stable and sufficient power throughout the flight. This section outlines the steps and considerations for effectively managing power distribution in your autonomous drone system.

Power Distribution Board (PDB) Setup

Primary Power Source:The LiPo battery serves as the primary power source for the drone. Select a LiPo battery with a sufficient voltage and capacity to meet the demands of all connected components. A common choice is a 3S (11.1V) or 4S (14.8V) LiPo battery, depending on your system’s total power requirements.

PDB Selection:Choose a Power Distribution Board (PDB) that can handle the total current draw of all components. The PDB should be rated for the combined current that the motors, ESCs, Raspberry Pi, Coral TPU, camera, and other peripherals will draw at peak load. Ensure that the PDB has enough outputs to connect all components directly or through voltage regulators.

Powering the Flight Controller (SpeedyBee F405 Mini)

Direct Connection:The SpeedyBee F405 Mini flight controller can be powered directly from the PDB via a 5V regulated output. The flight controller typically has a built-in power management system to regulate voltage for its internal components.

Voltage Regulation:Ensure that the PDB provides a stable 5V output to the flight controller. If the PDB’s 5V output is not stable or sufficient, consider using a separate 5V voltage regulator to provide a clean and consistent power supply.

Powering the Raspberry Pi

Power Requirements:The Raspberry Pi typically requires a stable 5V power supply, with a current draw of up to 2.5A depending on the model and connected peripherals.

Power Supply Options:

• Option 1: Power the Raspberry Pi directly from the PDB if it has a 5V output capable of supplying at least 2.5A.
• Option 2: Use a dedicated 5V/3A BEC (Battery Eliminator Circuit) or voltage regulator to power the Raspberry Pi, ensuring it receives a stable 5V supply without risking voltage drops that could cause brownouts during operation.

Brownout Prevention: To prevent brownouts, which can cause the Raspberry Pi to reboot or malfunction, ensure that the power supply has sufficient headroom above the maximum expected current draw. Adding capacitors near the power input can also help smooth out any voltage dips.

Powering the Coral TPU and Camera

• Coral TPU Power Requirements: The Coral TPU generally requires a 5V power supply, with a current draw of approximately 2–3A, depending on the workload.
• Camera Power Requirements: The camera module connected to the Coral TPU typically requires 3.3V or 5V, depending on the specific model. Check the camera’s specifications to determine the correct voltage.
• Power Supply Options:
• Option 1: Power both the Coral TPU and camera from the same 5V/3A BEC or voltage regulator used for the Raspberry Pi, ensuring that the combined current draw does not exceed the regulator’s capacity.
• Option 2: Use a separate 5V BEC for the Coral TPU and camera to isolate their power supply from the Raspberry Pi, reducing the risk of interference or power instability.

Power Management Considerations:

• When connecting the Coral TPU and camera, ensure that the PDB or BEC providing power is rated for the combined current draw of both components.
• If the camera operates at 3.3V, a voltage regulator stepping down from 5V to 3.3V may be required. Ensure that this regulator can handle the camera’s current requirements.

Powering Additional Peripherals (If Any)

• Additional Sensors or Modules:
• If your drone includes additional sensors, such as LiDAR, ultrasonic sensors, or telemetry modules, ensure that each peripheral has a dedicated and regulated power supply appropriate for its voltage and current needs.
• Use separate voltage regulators if needed to prevent power fluctuations from affecting sensitive components.

Power Distribution Diagram

Schematic Overview:

• The schematic should illustrate how power is distributed from the LiPo battery to each component via the PDB or individual voltage regulators.
• Key points include:
• LiPo battery connected to the PDB.
• PDB distributing 5V to the SpeedyBee F405 Mini, Raspberry Pi, Coral TPU, and camera.
• Dedicated BECs or voltage regulators providing stable power to the Raspberry Pi, Coral TPU, and camera, as necessary.

Testing and Verification

Initial Power-Up:

• Before assembling the entire drone, test each component individually to ensure they power up correctly and the voltage levels are stable.
• Measure the voltage at each power input (e.g., Raspberry Pi, Coral TPU, camera) to confirm that it matches the expected values.

Full System Load Test:

• With all components connected, simulate a full system load to verify that the power distribution remains stable. Monitor for any signs of power instability, such as voltage drops or overheating of voltage regulators.
• If possible, use a multimeter or oscilloscope to check for voltage dips or fluctuations during operation.

Flight Testing: Conduct a flight test to ensure that all components maintain stable power throughout different phases of flight, including takeoff, hovering, and maneuvers. Pay particular attention to the performance of the Raspberry Pi and Coral TPU under load.

By following these detailed power management guidelines, you can ensure that your autonomous drone system is both reliable and resilient, capable of operating all components efficiently without power-related issues.

Connecting the Coral TPU

Required Connections

• USB Connection: Connect the Coral TPU to the Raspberry Pi via USB. This will allow the Raspberry Pi to offload AI tasks to the TPU.

Optional Camera Setup

• Camera Module: If you plan to use the Coral TPU for real-time image processing, connect a camera module to the Coral TPU. Ensure the camera has a clear field of view.

Just a place holder … still needs to be done.

Just a place holder … this is not real

Software Setup on Raspberry Pi (Using Rust)

https://www.instructables.com/The-Drone-Pi/

Install Rust on Raspberry Pi

To start developing in Rust on your Raspberry Pi, you need to install the Rust toolchain.

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

Setting Up MAVLink in Rust

Add MAVLink Dependency:

• Add the mavlink-rs crate to your Cargo.toml:

[dependencies] mavlink = "0.9"

Write MAVLink Communication Code:

• Create a Rust script that initializes MAVLink communication between the Raspberry Pi and the SpeedyBee F405 Mini.

use mavlink::MavConnection;
use mavlink::common::MavMessage;

fn main() {
    let mut conn = MavConnection::open("/dev/serial0").unwrap();

    let arm_command = MavMessage::command_long(1, 1, 400, 0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
    conn.send(&arm_command).unwrap();
}

Integrating the Coral TPU with Rust

Add TensorFlow Lite Support:

• Use the tract-tensorflow crate for TensorFlow Lite model execution:

[dependencies]
tract-tensorflow = "0.15"

Load and Run AI Models:

• Write a Rust script that uses the Coral TPU to perform AI tasks, such as object detection.

use tract_tensorflow::prelude::*;

fn main() -> TractResult<()> {
    let model = tract_tensorflow::tensorflow()
        .model_for_path("path_to_your_model.tflite")?
        .with_input_fact(0, InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 224, 224, 3)))?
        .into_optimized()?
        .into_runnable()?;

    let input = Tensor::zero::<f32>(&[1, 224, 224, 3])?;
    let result = model.run(tvec!(input.into()))?;

    println!("Inference result: {:?}", result);
    Ok(())
}

Relay AI Data to MAVLink Commands:

• Use the AI output to influence the MAVLink commands sent to the flight controller. For example, if an object is detected, send a command to change the drone’s path.

Network Communication in Rust

Wi-Fi Communication:

• Use tokio and hyper crates to set up an HTTP server on the Raspberry Pi.

[dependencies]
tokio = { version = "1", features = ["full"] }
hyper = "0.14"

Example Code to Handle Commands:

use hyper::service::{make_service_fn, service_fn};
use hyper::{Body, Request, Response, Server};
use std::convert::Infallible;

async fn handle_request(_req: Request<Body>) -> Result<Response<Body>, Infallible> {
    Ok(Response::new(Body::from("Command received")))
}

#[tokio::main]
async fn main() {
    let addr = ([127, 0, 0, 1], 3000).into();

    let make_svc = make_service_fn(|_conn| {
        async { Ok::<_, Infallible>(service_fn(handle_request)) }
    });

    let server = Server::bind(&addr).serve(make_svc);

    if let Err(e) = server.await {
        eprintln!("Server error: {}", e);
    }
}

Detailed Overview of MAVLink Integration on the Mobile App and Raspberry Pi

MAVLink (Micro Air Vehicle Link) is a lightweight, efficient, and widely used communication protocol for controlling drones and other unmanned vehicles. It facilitates the exchange of commands, telemetry data, and other information between the drone’s flight controller, onboard computers like the Raspberry Pi, and external devices such as a mobile app. This section explains how MAVLink is implemented and utilized within both the mobile app and the Raspberry Pi to ensure seamless and reliable drone operation.

1. Understanding MAVLink Protocol

MAVLink Basics:

• MAVLink is a messaging protocol that allows for the structured exchange of information between different components of a drone system. It supports a wide range of messages, including commands for flight control, telemetry data, status reports, and parameter updates.
• The protocol operates on a packet-based system, where each message is encapsulated in a packet that includes the necessary data and metadata, such as message type, sequence number, and system ID.

Common MAVLink Messages:

• HEARTBEAT: A periodic message sent by each system component to indicate its presence and status.
• ATTITUDE: Provides information about the drone’s orientation, including pitch, roll, and yaw.
• GLOBAL_POSITION_INT: Contains GPS data such as latitude, longitude, altitude, and velocity.
• COMMAND_LONG: Used to send specific commands to the drone, such as arming the motors, taking off, or changing flight modes.
• PARAM_SET/PARAM_REQUEST_READ: Allows the mobile app or Raspberry Pi to read or modify parameters on the flight controller.

MAVLink on the Raspberry Pi

MAVLink Communication Setup on Raspberry Pi

MAVLink Library: On the Raspberry Pi, MAVLink communication is managed using the pymavlink library (for Python) or mavlink-rs (for Rust). These libraries provide the tools necessary to generate, parse, and send MAVLink messages.

Connection to the Flight Controller: The Raspberry Pi connects to the SpeedyBee F405 Mini flight controller via a serial connection (UART). This connection allows the Raspberry Pi to send and receive MAVLink messages directly to and from the flight controller.

Example Setup in Rust:

[dependencies]
mavlink = "0.9"

use mavlink::MavConnection;

fn main() -> mavlink::error::Result<()> {
    // Create a connection to the flight controller via serial port
    let mut connection = MavConnection::open("/dev/serial0", 115200)?;

    // Wait for a heartbeat message to confirm connection
    connection.wait_for_heartbeat(None)?;
    println!("Heartbeat received, connected to flight controller.");

    Ok(())
}

Generating and Sending MAVLink Commands: The Raspberry Pi generates MAVLink commands based on input received from the mobile app or other onboard sensors (e.g., GPS, camera).

Example Establishing Connection in Rust:

use mavlink::common::MavMessage;

fn arm_motors(connection: &mut MavConnection) -> mavlink::error::Result<()> {
    connection.send(&MavMessage::command_long(
        1, // target system
        1, // target component
        mavlink::common::MavCmd::MAV_CMD_COMPONENT_ARM_DISARM as u16, // command
        0, // confirmation
        1.0, // arm (1 to arm, 0 to disarm)
        0.0, 0.0, 0.0, 0.0, 0.0, 0.0
    ))?;
    println!("Motors armed.");
    Ok(())
}

fn main() -> mavlink::error::Result<()> {
    let mut connection = MavConnection::open("/dev/serial0", 115200)?;
    connection.wait_for_heartbeat(None)?;
    arm_motors(&mut connection)?;
    Ok(())
}

Arming the Motors:

use mavlink::common::MavMessage;

fn arm_motors(connection: &mut MavConnection) -> mavlink::error::Result<()> {
    connection.send(&MavMessage::command_long(
        1, // target system
        1, // target component
        mavlink::common::MavCmd::MAV_CMD_COMPONENT_ARM_DISARM as u16, // command
        0, // confirmation
        1.0, // arm (1 to arm, 0 to disarm)
        0.0, 0.0, 0.0, 0.0, 0.0, 0.0
    ))?;
    println!("Motors armed.");
    Ok(())
}

fn main() -> mavlink::error::Result<()> {
    let mut connection = MavConnection::open("/dev/serial0", 115200)?;
    connection.wait_for_heartbeat(None)?;
    arm_motors(&mut connection)?;
    Ok(())
}

Integrating MAVLink with Other Systems on the Raspberry Pi

Integration with GPS and Sensors:

• The Raspberry Pi can send GPS data to the flight controller via MAVLink, allowing the drone to navigate based on external GPS coordinates.
• It can also receive telemetry data (e.g., battery status, GPS coordinates) from the flight controller, which is then processed and possibly sent to the mobile app.

Processing AI Outputs: When the Coral TPU detects an obstacle, the Raspberry Pi generates an appropriate MAVLink command to alter the drone’s course. This command is sent to the flight controller to execute real-time adjustments.

Command Relay from Mobile App: The Raspberry Pi acts as a relay between the mobile app and the flight controller. Commands from the app, such as “come to me” or “land,” are received by the Raspberry Pi, translated into MAVLink commands, and sent to the flight controller.

//Sending GPS Data from the Raspberry Pi to the Flight Controller:
use mavlink::common::{MavMessage, MAV_FRAME_GLOBAL_RELATIVE_ALT_INT};

fn send_gps_command(connection: &mut MavConnection, lat: i32, lon: i32, alt: i32) -> mavlink::error::Result<()> {
    connection.send(&MavMessage::set_position_target_global_int(
        1, 1, MAV_FRAME_GLOBAL_RELATIVE_ALT_INT, 0b110111111000, 
        lat, lon, alt, 0, 0, 0, 0, 0, 0, 0, 0
    ))?;
    println!("Sent GPS command: lat {}, lon {}, alt {}", lat, lon, alt);
    Ok(())
}

fn main() -> mavlink::error::Result<()> {
    let mut connection = MavConnection::open("/dev/serial0", 115200)?;
    connection.wait_for_heartbeat(None)?;
    
    let user_latitude = 377749000;  // Example latitude (scaled by 1e7)
    let user_longitude = -1224194000;  // Example longitude (scaled by 1e7)
    let altitude = 1000;  // Example altitude in mm (1 meter = 1000 mm)
    
    send_gps_command(&mut connection, user_latitude, user_longitude, altitude)?;
    Ok(())
}

Handling Obstacle Detection and Sending Commands:

fn handle_obstacle(connection: &mut MavConnection) -> mavlink::error::Result<()> {
    // Assuming obstacle detected, command to move left
    connection.send(&MavMessage::command_long(
        1, 
        1, 
        mavlink::common::MavCmd::MAV_CMD_DO_SET_ROI as u16, 
        0, 
        0.0, 0.0, 0.0, 0.0, -5.0, 0.0, 0.0 // example to move left 5 units
    ))?;
    println!("Sent obstacle avoidance command.");
    Ok(())
}

fn main() -> mavlink::error::Result<()> {
    let mut connection = MavConnection::open("/dev/serial0", 115200)?;
    connection.wait_for_heartbeat(None)?;
    
    // Example: detect an obstacle and handle it
    handle_obstacle(&mut connection)?;
    Ok(())
}

Handling MAVLink Telemetry and Feedback

Telemetry Reception:

• The Raspberry Pi receives continuous telemetry from the flight controller, such as altitude, GPS location, and battery status.
• This data is processed and sent back to the mobile app for real-time monitoring by the user.

Feedback Loop: The Raspberry Pi creates a feedback loop, where it uses telemetry data to adjust commands sent to the flight controller. For example, if the drone drifts off course, the Raspberry Pi can send corrective commands based on the received GPS data.

Receiving Telemetry Data Example in Rust:

use reqwest::Client;
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new();
    let response = client.post("http://raspberry-pi-ip:3000/command")
        .json(&json!({
            "command": "NAVIGATE_TO_USER",
            "latitude": 37.7749,
            "longitude": -122.4194,
            "altitude": 10
        }))
        .send()
        .await?;

    println!("Command sent, status: {}", response.status());
    Ok(())
}

MAVLink on the Mobile App

Command Generation and Transmission

Generating Commands:

• The mobile app generates high-level commands based on user input, which are then formatted into structured JSON or directly into MAVLink messages, depending on the communication setup.
• These commands may include instructions like “take off,” “land,” “go to specific coordinates,” or “circle around an object.”

Transmitting Commands to Raspberry Pi:

• The app transmits these commands to the Raspberry Pi via a communication protocol such as HTTP, WebSocket, or Bluetooth.

Example Command Transmission:

use reqwest::Client;
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new();
    let response = client.post("http://raspberry-pi-ip:3000/command")
        .json(&json!({
            "command": "NAVIGATE_TO_USER",
            "latitude": 37.7749,
            "longitude": -122.4194,
            "altitude": 10
        }))
        .send()
        .await?;

    println!("Command sent, status: {}", response.status());
    Ok(())
}

MAVLink Command Encapsulation: If the mobile app generates MAVLink commands directly (e.g., using a MAVLink library), these commands are encapsulated in MAVLink packets and sent to the Raspberry Pi or directly to the flight controller if there’s a direct connection.

Real-Time Telemetry and Feedback

Receiving Telemetry Data: The mobile app receives telemetry data sent from the Raspberry Pi, which could include MAVLink messages detailing the drone’s current state (e.g., position, speed, battery life).

Receiving Telemetry Data in the Mobile App

use reqwest::Client;
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new();
    let response = client.post("http://raspberry-pi-ip:3000/command")
        .json(&json!({
            "command": "NAVIGATE_TO_USER",
            "latitude": 37.7749,
            "longitude": -122.4194,
            "altitude": 10
        }))
        .send()
        .await?;

    println!("Command sent, status: {}", response.status());
    Ok(())
}

Displaying Data to the User:

• The app processes this telemetry data and updates the user interface in real-time, allowing the user to monitor the drone’s status and make informed decisions.
• For instance, if the battery level is low, the app might suggest returning the drone to the base or landing.

Sending Feedback Commands: Based on the telemetry data, the app can generate new commands. For example, if the app detects that the drone is approaching a geofencing boundary, it can send a command to adjust the drone’s course.

Error Handling and Fail-Safe Mechanisms

Detecting Anomalies: The mobile app, in conjunction with the Raspberry Pi, monitors for anomalies such as loss of communication, low battery, or unexpected changes in telemetry data.

Fail-Safe Activation:

• If an error is detected, the mobile app can send immediate MAVLink commands to the drone to trigger fail-safe mechanisms, such as auto-landing or return-to-home functions.
• These commands are given priority and transmitted directly to the Raspberry Pi, which relays them to the flight controller.

Workflow: How MAVLink Connects the Mobile App and Raspberry Pi

User Input:The user provides input via the mobile app, either through voice commands or manual control. The app processes this input using the LLM and generates the appropriate commands.

Command Transmission:These commands are transmitted to the Raspberry Pi via Wi-Fi, Bluetooth, or another communication protocol. The Raspberry Pi receives these commands and translates them into MAVLink messages.

Flight Control:The Raspberry Pi sends the MAVLink commands to the SpeedyBee F405 Mini flight controller. The flight controller executes these commands, adjusting the drone’s flight parameters accordingly.

Telemetry Feedback:The flight controller sends telemetry data back to the Raspberry Pi, which processes it and forwards it to the mobile app. The app displays this data to the user, updating the UI in real-time.

Dynamic Adjustments: Based on the received telemetry, the mobile app or Raspberry Pi may generate new commands to adapt to the drone’s current state or environmental conditions. This loop continues until the command sequence is complete.

MAVLink plays a crucial role in the communication between the mobile app, Raspberry Pi, and the flight controller. On the Raspberry Pi, MAVLink handles the low-level control of the drone, converting high-level commands from the mobile app into executable instructions for the flight controller. The mobile app acts as a user interface, processing user commands, transmitting them to the Raspberry Pi, and displaying real-time telemetry data to the user. Together, these systems create a robust and flexible control architecture, allowing for complex drone operations and real-time decision-making.

Long Range without Wifi or BlueTooth

Using a Companion Device (Raspberry Pi or ESP32) with AirPort for Drone Communication

In scenarios where your drone project involves an Android or iOS mobile application but requires the extended range and low-latency communication provided by ExpressLRS with AirPort firmware, a companion device like a Raspberry Pi or ESP32 can serve as an essential bridge. This section details how to set up this companion device to facilitate communication between the mobile app and the drone, using AirPort for reliable, long-range data transmission.

System Overview

The system integrates the following key components:

1. Mobile App (Android/iOS): The user interface for sending commands to the drone and receiving telemetry data.
2. Companion Device (Raspberry Pi or ESP32): Acts as a bridge, translating communication between the mobile app and the drone via AirPort.
3. ExpressLRS with AirPort Firmware: Facilitates long-range, low-latency wireless communication between the companion device and the drone.
4. Drone: Equipped with a flight controller (e.g., SpeedyBee F405 Mini) and ExpressLRS receiver, capable of processing commands and sending telemetry back to the companion device.

Hardware Setup

Hardware Components Needed:

• Raspberry Pi (e.g., Raspberry Pi 4) or ESP32 Development Board
• ExpressLRS TX Module: Connected to the companion device via USB or UART.
• ExpressLRS RX Module: Installed on the drone, connected to the flight controller via a UART port.
• Power Supply: Ensure that both the Raspberry Pi/ESP32 and the ExpressLRS modules are properly powered.

Wiring and Connections:

• Connecting the ExpressLRS RX Module to the Drone:
• Connect the TX and RX pins of the ExpressLRS RX module to the corresponding UART pins on the flight controller.
• Power the RX module with 5V and GND connections.

Connecting the ExpressLRS TX Module to the Companion Device:

Raspberry Pi: Connect the TX module to one of the UART interfaces on the Raspberry Pi. Alternatively, use a USB-to-UART adapter if the TX module supports USB communication.

ESP32: Connect the TX module to one of the available UART pins on the ESP32.

Companion Device Setup: Ensure that the Raspberry Pi or ESP32 is configured to communicate with the mobile app over Wi-Fi or Bluetooth.

Software Setup on the Companion Device

Install Required Software (Raspberry Pi):

Install Rust:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

Setup Serial Communication:Ensure the serial ports are enabled on the Raspberry Pi, and you have access to the correct UART interface.

Implementing Communication in Rust:

This section covers how to establish communication between the mobile app, the companion device, and the drone using Rust.

Serial Communication with ExpressLRS:

Example Code for Serial Setup on Raspberry Pi

use std::io::{self, Write};
use std::time::Duration;
use serialport::prelude::*;

fn main() -> io::Result<()> {
    let mut settings: SerialPortSettings = Default::default();
    settings.baud_rate = 115200;
    settings.timeout = Duration::from_millis(10);

    // Open the serial port for communication with the ExpressLRS module
    let port_name = "/dev/serial0";  // Replace with the appropriate serial port
    let mut port = serialport::open_with_settings(&port_name, &settings)?;

    // Example: Send a test command to the drone
    port.write_all(b"MAVLINK COMMAND\n")?;
    port.flush()?;
    
    // Read response from the drone
    let mut buffer: Vec<u8> = vec![0; 32];
    match port.read(&mut buffer) {
        Ok(n) => println!("Received response: {:?}", &buffer[..n]),
        Err(e) => eprintln!("Failed to read from port: {:?}", e),
    }

    Ok(())
}

Handling Commands from the Mobile App

Receive Commands Over Wi-Fi/Bluetooth: You can set up a simple HTTP server on the Raspberry Pi/ESP32 to receive commands from the mobile app.

Example using the warp web framework in Rust:

use warp::Filter;
use std::sync::{Arc, Mutex};

#[tokio::main]
async fn main() {
    let shared_port = Arc::new(Mutex::new(serialport::open("/dev/serial0").unwrap()));

    let route = warp::path("command")
        .and(warp::post())
        .and(warp::body::json())
        .and(warp::any().map(move || shared_port.clone()))
        .map(|command: serde_json::Value, port: Arc<Mutex<Box<dyn serialport::SerialPort>>>| {
            let command_str = format!("{}", command["command"]);
            let mut port = port.lock().unwrap();
            port.write_all(command_str.as_bytes()).unwrap();
            warp::reply::json(&"Command sent".to_string())
        });

    warp::serve(route).run(([0, 0, 0, 0], 3030)).await;
}

Parsing and Relaying Commands to the Drone: Once the Raspberry Pi or ESP32 receives a command from the mobile app, it translates this into MAVLink commands (or any other protocol) and sends it via the serial interface to the ExpressLRS module. The ExpressLRS module then transmits the command to the drone’s flight controller.

Receiving Telemetry Data: The flight controller sends telemetry data back through the ExpressLRS RX module to the companion device.

Example Code to Read Telemetry Data:

use warp::Filter;
use std::sync::{Arc, Mutex};

#[tokio::main]
async fn main() {
    let shared_port = Arc::new(Mutex::new(serialport::open("/dev/serial0").unwrap()));

    let route = warp::path("command")
        .and(warp::post())
        .and(warp::body::json())
        .and(warp::any().map(move || shared_port.clone()))
        .map(|command: serde_json::Value, port: Arc<Mutex<Box<dyn serialport::SerialPort>>>| {
            let command_str = format!("{}", command["command"]);
            let mut port = port.lock().unwrap();
            port.write_all(command_str.as_bytes()).unwrap();
            warp::reply::json(&"Command sent".to_string())
        });

    warp::serve(route).run(([0, 0, 0, 0], 3030)).await;
}

Sending Telemetry Data to the Mobile App: After processing the telemetry data from the drone, the companion device can send this data back to the mobile app via Wi-Fi or Bluetooth, allowing the user to monitor the drone’s status in real-time.

Mobile App Interaction

Command Transmission: The mobile app sends commands over Wi-Fi or Bluetooth to the Raspberry Pi/ESP32, which relays these commands to the drone via the ExpressLRS system.

Telemetry Display: The mobile app receives telemetry data from the Raspberry Pi/ESP32, enabling real-time monitoring and adjustments based on the drone’s status.

User Interface: The mobile app provides a user-friendly interface for controlling the drone, sending commands, and viewing telemetry data. The app can be built using frameworks like Flutter or React Native for cross-platform compatibility.

Using a Raspberry Pi or ESP32 as a companion device bridges the gap between your mobile app and the drone, enabling long-range, low-latency communication via ExpressLRS with AirPort firmware. This setup is ideal for projects requiring extended operational range and reliable data transmission. With the companion device handling the communication between the mobile app and the drone, you can leverage the power of ExpressLRS while maintaining the flexibility and portability of controlling the drone via a mobile app.

AI Drone

Configuring the SpeedyBee F405 Mini

Flash Firmware: Flash the SpeedyBee F405 Mini with appropriate firmware like Betaflight or INAV.

Configure MAVLink: Set up the UART port to communicate using MAVLink.

Calibrate and Tune: Perform sensor calibration and PID tuning to ensure stable flight performance.

Mobile Application Development

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Voice Command Interface

Implement Speech Recognition: Use Android’s Gemini speech recognition API to capture and process voice commands.

Send Commands to Raspberry Pi: Use Retrofit or a similar library to send commands over HTTP or Bluetooth to the Raspberry Pi. (see below)

System Operation

Detailed Workflow: Drone Navigation Using GPS and Camera for Object Detection

In this scenario, the drone and the Android phone both have GPS, and the drone’s camera is used for object detection and collision avoidance. The command “come to me” will be processed as follows:

Command Interpretation: Android/iOS App and LLM Processing

Voice Command Recognition:

• The user speaks “come to me” into the Android app.
• The Android app uses built-in voice recognition to convert the voice command into text.

LLM Processing:

• The text command is processed by the LLM running in the mobile app. The LLM interprets the command to mean that the drone should navigate to the user’s current location.

iOS:

GitHub - eastriverlee/LLM.swift: LLM.swift is a simple and readable library that allows you to…

Android:

Gemini Nano

• The app then fetches the current GPS coordinates of the user’s phone.

Instruction to the Raspberry Pi:

• The Android/iOS app sends a high-level command to the Raspberry Pi on the drone, including the user’s GPS coordinates.
• The command might look something like this:

{
  "command": "NAVIGATE_TO_USER",
  "coordinates": {
    "latitude": 37.7749,
    "longitude": -122.4194,
    "altitude": 10 // Altitude in meters
  }
}

• This command is sent via Wi-Fi or Bluetooth to the Raspberry Pi.

Raspberry Pi Processing and Navigation Command Execution

Receiving the Command:

• The Raspberry Pi receives the command and parses the JSON message to extract the GPS coordinates of the user.

GPS-Based Navigation:

• The Raspberry Pi converts the received GPS coordinates into a MAVLink command that the SpeedyBee F405 Mini flight controller can understand.
• Example MAVLink Command in Rust:

use mavlink::common::{MavMessage, MAV_FRAME_GLOBAL_RELATIVE_ALT_INT};

fn navigate_to_user(lat: i32, lon: i32, alt: i32) {
    let mut conn = MavConnection::open("/dev/serial0").unwrap();
    let msg = MavMessage::set_position_target_global_int(
        1, 1, MAV_FRAME_GLOBAL_RELATIVE_ALT_INT, 0b110111111000, lat, lon, alt, 0, 0, 0, 0, 0, 0, 0, 0);
    conn.send(&msg).unwrap();
}

fn main() {
    let user_latitude = 377749000;  // Example latitude (scaled by 1e7)
    let user_longitude = -1224194000;  // Example longitude (scaled by 1e7)
    let altitude = 1000;  // Example altitude in mm (1 meter = 1000 mm)
    navigate_to_user(user_latitude, user_longitude, altitude);
}

• The drone begins moving towards the user’s location based on GPS data.

Object Detection and Collision Avoidance Using the Camera

Activating the Camera:

• As the drone starts navigating, the Raspberry Pi activates the camera connected to the Coral TPU for real-time object detection.

Real-Time Object Detection:

• The camera continuously captures video, which is processed by an AI model running on the Coral TPU. The model is trained to detect obstacles such as trees, buildings, or other objects.

Example TensorFlow Lite Integration in Rust:

use tract_tensorflow::prelude::*;

fn detect_objects() -> TractResult<()> {
    let model = tract_tensorflow::tensorflow()
        .model_for_path("path_to_your_model.tflite")?
        .with_input_fact(0, InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 300, 300, 3)))?
        .into_optimized()?
        .into_runnable()?;

    let input = Tensor::zero::<f32>(&[1, 300, 300, 3])?;
    let result = model.run(tvec!(input.into()))?;

    // Process detection results and make decisions
    println!("Detection result: {:?}", result);
    Ok(())
}

• If an object is detected in the drone’s path, the AI model alerts the Raspberry Pi.

Collision Avoidance:

• Upon detecting an obstacle, the Raspberry Pi recalculates the drone’s path to avoid the obstacle while still moving towards the user’s location.
• Adjustments are made in real-time by sending new MAVLink commands to the flight controller, such as altering altitude or changing direction.

Navigation Towards the User

Combining GPS and Visual Data:

• The drone uses GPS data to head towards the user and relies on the camera for collision avoidance.
• As the drone approaches the user, the camera can also be used to visually confirm the user’s presence and make fine adjustments to the drone’s position.

GPS and Camera Data Fusion: Detailed Process for Navigation and Obstacle Avoidance

In an autonomous drone system that combines GPS data for navigation with camera-based object detection for collision avoidance, it is crucial to define how these two data sources are integrated and prioritized. The process involves continuous monitoring, decision-making, and dynamic adjustments to ensure the drone safely and accurately navigates toward the user while avoiding obstacles. This section expands on the mechanism by which the drone switches between or fuses GPS data and camera input during flight.

Overview of Data Fusion

The GPS provides global positioning data, allowing the drone to navigate to a specific set of coordinates (e.g., the user’s location). However, GPS alone is insufficient for safe navigation in complex environments, especially where obstacles may be present. The camera, coupled with AI-based object detection (using the Coral TPU), offers a real-time local awareness that complements the GPS by identifying and avoiding obstacles.

Prioritization and Coordination of GPS and Camera Data

GPS as the Primary Navigation Source:

Waypoint Navigation:

• The GPS is the primary tool for navigation when the drone is en route to the user. The Raspberry Pi uses the GPS coordinates provided by the mobile app (via the user’s phone) to set a destination waypoint.
• The drone’s flight controller continuously receives these GPS coordinates via MAVLink commands, directing the drone along the most direct route to the user.

Course Adjustment:

• While GPS provides the overarching direction, the drone’s course is not fixed. The system regularly checks the drone’s current position against the target coordinates, adjusting the flight path as needed to stay on course.

Camera Data for Obstacle Detection and Avoidance:

Continuous Monitoring:

• As the drone navigates towards the GPS waypoint, the camera is continuously active, capturing the environment in real-time. The Coral TPU processes this visual data to detect obstacles within the drone’s path.

Obstacle Detection and Classification:

• The AI model on the Coral TPU identifies potential obstacles, such as trees, buildings, or people. It classifies these objects based on their distance, size, and relevance to the drone’s flight path.
• The camera can detect objects directly in the drone’s path, as well as those in the periphery that may become a threat as the drone moves.

Immediate Threats:

• If the camera detects an obstacle that poses an immediate threat (e.g., a tree directly ahead), the Raspberry Pi temporarily prioritizes the camera data over GPS.
• The drone adjusts its flight path in real-time to avoid the obstacle. This might involve altering altitude, changing direction, or even temporarily pausing forward movement until a clear path is found.

Dynamic Data Fusion:

Fusion Algorithm:

• A fusion algorithm runs on the Raspberry Pi, integrating both GPS and camera data. The algorithm continuously evaluates the situation, making real-time decisions on whether to follow the GPS coordinates directly or to adjust the path based on visual input from the camera.
• The algorithm operates on a set of rules and priorities. For example:

Rule 1: Follow the GPS coordinates unless an obstacle is detected within a certain proximity.

Rule 2: If an obstacle is detected, calculate an alternate route that deviates from the current path as little as possible, ensuring that the drone remains on course towards the GPS target.

Proximity-Based Decision Making:

• As the drone approaches the user (i.e., as the distance to the target GPS coordinates decreases), the system gradually increases the weight given to camera data.
• In an open field with few obstacles, GPS remains the primary data source. However, in more cluttered environments (e.g., near buildings or trees), the camera data may take precedence, guiding the drone to make more frequent course adjustments to avoid collisions.

GPS and Camera Interaction Near the User:

Final Approach:

• During the final approach, when the drone is within a close range of the user (e.g., within 10 meters), the camera becomes the dominant source of navigation data.
• The GPS ensures that the drone is in the general vicinity, but the camera is used to fine-tune the drone’s position, ensuring it hovers directly in front of or above the user.

Visual Confirmation:

• The camera can visually confirm the user’s presence, allowing the drone to make precise adjustments based on the user’s exact location, posture, and any potential obstructions around them.
• If the camera identifies the user within its field of view, the drone may enter a hover mode or slowly circle to maintain a clear line of sight, awaiting further commands.

Continuous Course Adjustment Based on Real-Time Data

Real-Time Processing:

• The drone continuously processes GPS and camera data in real-time, with the Raspberry Pi acting as the central hub for decision-making.
• The system ensures that even if the GPS signal fluctuates or the environment changes (e.g., new obstacles appear), the drone adapts its course dynamically to maintain a safe and effective flight.

Data Logging and Feedback:

• The system logs GPS and camera data during the flight, allowing for post-flight analysis and further optimization of the fusion algorithm.
• Feedback loops are implemented to correct the drone’s path continuously, ensuring minimal deviation from the target while maximizing safety.

Handling Edge Cases and Complex Scenarios

Obstacle Clusters: In situations where multiple obstacles are detected in close proximity, the drone’s system prioritizes safety over directness. The drone may temporarily deviate significantly from the GPS path to navigate through a clear area before returning to its course.

GPS Signal Loss: If the GPS signal is lost (e.g., due to interference or being indoors), the system relies entirely on the camera for navigation. In such cases, the drone may switch to a mode where it slowly advances based on visual cues, maintaining a safe altitude and speed until the GPS signal is reacquired.

User Movement: If the user moves after the “come to me” command is issued, the system re-evaluates the target GPS coordinates. If the user moves within the camera’s field of view, the drone may switch entirely to visual tracking, following the user based on their movement, while periodically checking for the latest GPS coordinates.

Summary of GPS and Camera Data Fusion

• Primary Navigation: GPS provides the primary navigation method, guiding the drone to the general area of the user’s location.
• Obstacle Avoidance: The camera and Coral TPU handle obstacle detection and avoidance, allowing the drone to safely navigate through complex environments.
• Dynamic Adjustments: The fusion algorithm on the Raspberry Pi dynamically balances GPS data with camera input, prioritizing safety and accuracy.
• Final Positioning: The camera ensures precise positioning near the user, adjusting the drone’s location to account for the user’s exact position and any nearby obstacles.

This integration of GPS and camera data ensures that the drone can navigate effectively and safely in a variety of environments, responding intelligently to both global navigation needs and local obstacle challenges.

Final Approach and Hovering:

• Once the drone reaches the vicinity of the user’s GPS coordinates, it hovers at a safe altitude.
• The Raspberry Pi might use visual feedback from the camera to make any final adjustments, ensuring the drone is positioned accurately near the user.

System Feedback:

• The Raspberry Pi sends a status update back to the Android app, confirming that the drone has arrived at the user’s location.
• The app could provide visual or auditory feedback to the user, such as “Drone has arrived.”

Continuous Monitoring and Further Commands

Continuous Monitoring:

• While hovering, the drone continues to monitor its surroundings using the camera and the GPS, ready to respond to any new obstacles or environmental changes.

Handling New Commands:

• The user can issue further voice commands through the Android app, such as “land,” “take a photo,” or “return to base.”
• These commands are processed similarly, with the LLM interpreting the command and sending appropriate instructions to the Raspberry Pi.

In this system, the drone effectively combines GPS and visual data from the camera to navigate towards the user upon receiving the “come to me” command. The Android app and LLM handle the initial interpretation of the command and relay the necessary GPS coordinates to the Raspberry Pi. The Raspberry Pi then controls the drone’s flight using MAVLink commands and employs the Coral TPU for real-time object detection and collision avoidance.

This setup ensures that the drone can autonomously and safely navigate to the user, making it highly adaptable to both indoor and outdoor environments, regardless of obstacles. The system’s modularity allows for further enhancements, such as more sophisticated AI models for complex environments or additional sensors for even greater autonomy.

Reference: Complex Commands with Ambiguous

LLM API Integration: Use an API to connect the Android app to an LLM, allowing it to interpret complex commands or provide conversational feedback.

Command Mapping: Map recognized voice commands to specific MAVLink commands to control the drone.

Handling Voice Command Complexity and Real-Time Decision-Making

When integrating an LLM (Large Language Model) into the mobile app for interpreting voice commands, especially in the context of an autonomous drone, it’s crucial to address how the system deals with ambiguities and makes real-time decisions. This section delves into how the system handles complex commands, ambiguous scenarios, and unexpected events, ensuring that the drone operates safely and effectively in various environments.

Understanding Voice Command Complexity

Interpretation of Complex Commands

• Natural Language Processing (NLP): The mobile app leverages the LLM to interpret natural language voice commands issued by the user. The LLM is trained to understand context, intent, and nuances in human speech, allowing it to process complex commands like “come to me” or “fly around the obstacle and land.”

Ambiguity in Commands: When a user issues a command that is ambiguous or open to interpretation, such as “come to me” in an environment with multiple possible paths, the LLM must disambiguate the command. The system does this by considering contextual data, such as the drone’s current environment, the user’s location, and any known obstacles.

Contextual Awareness: The LLM uses contextual awareness to interpret the command. For example, if the user is indoors and the drone is aware of potential obstacles (like walls or furniture), the LLM can infer that the drone should navigate cautiously and potentially use visual data from the camera to find a safe path.

Handling Ambiguous Scenarios

Multiple Paths and Obstacles: In scenarios where the drone must navigate through an environment with multiple potential paths (e.g., hallways, rooms, or outdoor areas with trees), the LLM provides a preliminary path based on GPS data. However, it leaves room for real-time adjustments based on visual input from the drone’s camera.

Decision-Making Process:The mobile app sends an initial command to the drone, such as “navigate towards the user’s GPS coordinates.” As the drone encounters obstacles or multiple paths, the LLM on the app continuously updates the command, incorporating feedback from the drone’s sensors and camera to refine the path.

• Example: If the LLM detects that the drone might encounter an obstacle along its current path, it can issue a refined command, such as “take the left path around the obstacle.”

User Clarification: In cases where the LLM cannot confidently disambiguate a command, it may request clarification from the user via the mobile app. For instance, if the command “come to me” is too vague in a complex environment, the app might respond with, “I detected multiple paths. Would you like the drone to take the left path or the right path?”

Real-Time Decision-Making in the Mobile App

Dynamic Interaction Between the LLM and the Drone

Continuous Communication: The mobile app maintains continuous communication with the drone, processing data received from the drone’s sensors, GPS, and camera in real-time. This data is fed back into the LLM to adjust the drone’s path dynamically.

Handling Unexpected Scenarios: When the drone encounters unexpected scenarios, such as an obstacle that wasn’t previously detected by the camera or an abrupt change in the environment (e.g., a moving object), the LLM processes this information and updates the command to the drone.

• Example: If the drone suddenly detects a moving object (like a person walking), the LLM may instruct the drone to hover and wait for the path to clear or to recalibrate its route around the moving object.

Prioritization of Commands

Safety as a Priority: The LLM prioritizes safety above all other factors. If any ambiguity or potential danger is detected, the LLM’s default behavior is to instruct the drone to slow down, hover, or take a safer route. For example, if the drone cannot determine the safest path due to conflicting data, it may stop and wait for further instructions.

Path Recalculation: The mobile app can dynamically recalculate the path based on real-time input. If the LLM detects that the drone’s current path is no longer viable (e.g., due to an unexpected obstacle), it recalculates the route using both GPS data and visual input. The recalculated path is then sent back to the drone as a new set of instructions.

Error Handling and Fail-Safes: In the event of a critical error or loss of communication, the LLM can activate predefined fail-safes. For example, if the drone encounters an environment it cannot navigate safely, the LLM might instruct it to return to its starting point or land in a safe area.

Real-Time Feedback and Adaptation

Visual Feedback Loop: The mobile app and LLM create a feedback loop where visual data from the drone’s camera is continuously analyzed to adjust the flight path. This loop allows the system to adapt to dynamic environments, such as avoiding newly detected obstacles or adjusting altitude to maintain a clear path.

Command Refinement: As the drone executes commands, the LLM refines these commands based on real-time data. For instance, if the drone is flying through a narrow corridor, the LLM might adjust the drone’s speed and direction more frequently to ensure it avoids walls and other obstacles.

User Notifications: The mobile app can notify the user of the drone’s progress or any issues encountered. For example, the app might send a notification saying, “Obstacle detected, recalculating route,” or “Drone hovering, awaiting further instructions.”

Continuous Learning and Improvement

Adaptive Learning: Over time, the LLM can adapt to the user’s preferences and the specific environments the drone operates in. This learning process allows the LLM to make more informed decisions and improve its handling of ambiguous or complex commands.

Logging and Analysis: The mobile app can log each flight’s data, including command inputs, LLM decisions, and drone responses. This data can be analyzed post-flight to refine the LLM’s algorithms, improving future performance.

Practical Example: Handling a “Come to Me” Command Indoors

• Initial Command: The user, located in a large indoor area with multiple rooms, issues the command “come to me.”
• LLM Interpretation: The LLM in the mobile app processes the command and understands that the drone needs to navigate to the user’s GPS coordinates. However, the LLM also recognizes that the environment might contain obstacles like walls and furniture.
• Dynamic Navigation: As the drone begins navigating towards the GPS coordinates, it uses its camera to detect and avoid obstacles. The LLM continuously updates the drone’s path, instructing it to take turns, adjust altitude, or slow down as needed.
• Handling Ambiguity:If the drone encounters a hallway with two possible routes to the user, the LLM might either select the most likely path based on available data or ask the user for clarification via the app.
• Obstacle Encounter: Midway through the flight, the drone detects a large object blocking its path. The LLM quickly recalculates the route, instructing the drone to take a detour around the obstacle. The drone then resumes its original course toward the user’s coordinates.
• Final Approach: As the drone nears the user’s location, the LLM prioritizes camera data to ensure the drone doesn’t collide with any small, unanticipated obstacles. The drone hovers near the user and awaits further commands.

Summary

The integration of an LLM into the mobile app enables the drone to handle complex voice commands with contextual understanding and dynamic decision-making. By continuously processing GPS and visual data, the LLM can navigate the drone safely, even in environments with multiple paths or unexpected obstacles. The system’s ability to request clarification, prioritize safety, and adapt to real-time inputs makes it a robust solution for autonomous drone operation in diverse scenarios.

Testing and Deployment

Initial Testing

Command Testing: Test the communication between the Android app and Raspberry Pi using basic commands like “take off” and “land.”

Integration Testing: Verify that the AI processing on the Coral TPU correctly influences drone behavior.

Flight Testing

Controlled Environment: Perform test flights in a safe area to ensure that all components work together seamlessly.

Fine-Tuning: Adjust the PID settings, command mappings, and AI parameters to optimize performance.

Conclusion

By following this guide, you’ll be able to build a sophisticated autonomous drone system that can be controlled via voice commands through an Android application. This system leverages the processing power of the Raspberry Pi, the AI capabilities of the Coral TPU, and the robust MAVLink communication protocol to create a versatile and

powerful platform for drone applications.

References:

Luxonis Documentation

Roboflow: Computer vision tools for developers and enterprises