How to Use ChatGPT to control Drones

9 min readFeb 26, 2023

Controlling drones is becoming an increasingly popular application of artificial intelligence (AI) and natural language processing (NLP) technologies. In this post, I will explore how ChatGPT, a large language model trained by OpenAI, can be used to control drones by accessing object detection and distance data through APIs, and how we can leverage special prompting structures, high-level APIs, and human feedback in text to improve the accuracy and efficiency of our control system.

A surveillance drone flying over a meadow land

Accessing Object Detection and Distance Data through APIs

To control drones effectively, we need access to real-time information about the drone’s surroundings, including the location and distance of objects in its path. Object detection and distance estimation can be achieved through computer vision techniques using cameras mounted on the drone, but this requires significant computational resources and is not always reliable, especially in low-light or low-visibility conditions.

Alternatively, we can use APIs provided by third-party services such as Google Cloud Vision, Microsoft Azure Computer Vision, or Amazon Rekognition to perform object detection and distance estimation on images captured by the drone’s cameras. These APIs can be easily integrated with ChatGPT to provide real-time feedback on the drone’s surroundings.

To use these APIs, we need to provide an API key and the URL for the API endpoint. We can then send images from the drone’s cameras to the API using the requests module in Python and receive the results in JSON format. Here’s an example of how to use the Google Cloud Vision API to detect objects in an image:

import requests
import json

api_key = 'YOUR_API_KEY'
api_url = 'https://vision.googleapis.com/v1/images:annotate'

image_url = 'https://example.com/image.jpg'

headers = {'Content-Type': 'application/json'}
data = {
    'requests': [
        {
            'image': {
                'source': {'imageUri': image_url}
            },
            'features': [
                {'type': 'LABEL_DETECTION'},
                {'type': 'OBJECT_LOCALIZATION'}
            ]
        }
    ]
}

params = {'key': api_key}

response = requests.post(api_url, params=params, headers=headers, data=json.dumps(data))
results = json.loads(response.text)

# Process the results

The response from the API contains a list of labels and object localizations detected in the image, along with their confidence scores and bounding boxes. We can then use this information to control the drone’s movements and avoid collisions with obstacles.

Leveraging Special Prompting Structures and High-Level APIs

To control the drone through natural language input, we can use ChatGPT’s special prompting structures and high-level APIs to extract the user’s intent and generate appropriate commands for the drone. For example, we can use the GPT-3 API provided by OpenAI to generate natural language responses to user input and translate these responses into drone control commands.

Here’s an example of how to use the GPT-3 API to generate a response to user input:

import openai
import re

openai.api_key = 'YOUR_API_KEY'

def generate_response(prompt):
    response = openai.Completion.create(
        engine='davinci',
        prompt=prompt,
        max_tokens=1024,
        n=1,
        stop=None,
        temperature=0.5,
    )
    
    text = response.choices[0].text
    text = re.sub('[^a-zA-Z0-9 .,-]+', '', text)
    
    return text

In this example, we use the Davinci engine provided by OpenAI to generate a natural language response to the user’s input, given by the prompt argument. We set the maximum number of tokens to 1024 and the temperature to 0.5 to balance the trade-off between accuracy and diversity in the generated responses.

We can then parse the response text to extract the user’s intent and generate appropriate drone control commands. For example, if the user asks the drone to “fly forward”, we can generate a command to move the drone forward using the object detection and distance data obtained from the APIs.

Incorporating Human Feedback in Text

One challenge in using ChatGPT to control drones is ensuring the accuracy of the generated commands. To address this, we can incorporate human feedback in text to correct errors and improve the accuracy of the control system over time.

We can use a combination of active learning and reinforcement learning techniques to train ChatGPT to generate more accurate commands based on feedback from human operators. For example, we can use a reward function to encourage ChatGPT to generate commands that lead to successful drone movements and penalize commands that lead to collisions or other errors.

Here’s an example of how to incorporate human feedback in text to improve the accuracy of the control system:

def get_feedback(prompt, response):
    # Ask the user for feedback on the generated response
    feedback = input('Was the response accurate? (y/n)')
    
    if feedback == 'y':
        reward = 1
    else:
        reward = -1
    
    # Update the reward function
    update_reward_function(prompt, response, reward)

In this example, we ask the user for feedback on the generated response and update the reward function accordingly. Over time, the reward function will guide ChatGPT to generate more accurate commands based on feedback from human operators.

Zero-shot planning

Zero-shot planning is a technique that enables a drone to plan its path without prior knowledge of the environment. By utilizing object detection and distance data from the drone’s camera, we can improve the accuracy and efficiency of drone operations.

To implement this technique, we first need to initialize the drone object using the drone API. Then, we can define the target object class, such as “person”, and the minimum distance threshold in meters. We also need to specify the maximum number of steps the drone can take before returning to its home position.

# Initialize drone object
drone = drone_api.Drone()

# Define target object class
target_class = "person"

# Define minimum distance threshold in meters
min_distance = 10

# Define maximum number of steps
max_steps = 100

# Initialize current step counter
current_step = 0

Next, we need to create a loop that will continue until the target object is found or the maximum number of steps is reached. Inside the loop, we will get the current image from the drone’s camera and detect objects in the image using OpenCV. We will then check if the target object class is detected.

If the target object class is detected, we will get the bounding box coordinates of the target object and calculate the center coordinates and the distance to the object using the Pythagorean theorem. We will then get the actual distance to the target object using the drone API and check if it is below the minimum threshold. If the actual distance is below the minimum threshold, we will stop moving forward and land safely.

If the actual distance is above the minimum threshold, we will move forward towards the target object. If the target object class is not detected, we will rotate randomly until the target object class is detected. We will increment the current step counter in each iteration and check if the maximum number of steps is reached. If the maximum number of steps is reached, we will stop moving and return to the home position.

# Loop until target object is found or maximum number of steps is reached
while True:
    # Get current image from drone's camera
    image = drone.get_image()

    # Detect objects in image using OpenCV
    objects = cv2.detect_objects(image)

    # Check if target object class is detected
    if target_class in objects:
        # Get bounding box coordinates of target object
        x1, y1, x2, y2 = objects[target_class]

        # Calculate center coordinates of target object
        cx = (x1 + x2) / 2
        cy = (y1 + y2) / 2

        # Calculate distance to target object using Pythagorean theorem
        dx = cx - image.shape[1] / 2
        dy = cy - image.shape[0] / 2
        dz = np.sqrt(dx**2 + dy**2)

        # Get actual distance to target object using API
        actual_distance = drone.get_distance(dz)

        # Check if actual distance is below minimum threshold
        if actual_distance < min_distance:
            # Stop moving forward and land safely
            drone.stop()
            drone.land()
            break
        else:
            # Move forward towards target object
            drone.forward(actual_distance - min_distance)

    else:
        # Rotate randomly until target object class is detected 
        angle = np.random.randint(0, 360) # Random angle in degrees
        drone.rotate(angle)

    # Increment current step counter
    current_step += 1

    # Check if maximum number of steps is reached
    if current_step == max_steps:
        # Stop moving and return to home position 
        drone.stop()
        drone.return_home()
        break

Aside from object detection and distance data, there are several other aspects to be considered for ChatGPT to effectively control drones:

Drone API: The drone API provides a set of instructions for controlling the drone’s movement and accessing its sensors. It is important to choose an API that is compatible with the drone being used and provides the necessary functionality for the task at hand.
Image Processing: Object detection and distance data are obtained by processing the image data from the drone’s camera. Therefore, it is important to have a robust image processing pipeline to accurately detect and track objects in real-time.
Communication Latency: Drones often operate in remote or hard-to-reach locations, and communication latency can be a major issue when controlling a drone from a remote location. It is important to have a reliable and fast communication system to ensure real-time control of the drone.
Safety: Drones can be dangerous and pose a risk to people and property if not operated safely. Therefore, it is important to follow safety guidelines and regulations when operating a drone, and to incorporate safety features such as obstacle detection and avoidance in the control system.
Battery Life: Drones rely on batteries for power, and battery life can be a limiting factor in the duration of a drone flight. It is important to consider the drone’s battery life when planning the mission and to incorporate battery monitoring and management features in the control system.
Environmental Factors: Environmental factors such as wind, temperature, and humidity can affect the performance and stability of a drone. It is important to consider these factors and adjust the control system accordingly to ensure safe and effective operation.

To give you a glimpse of how this can be achieved, here is an example program in Python that demonstrates how ChatGPT can be used to control a drone:

import cv2
import numpy as np
import time
import openai
import dronekit

# Set up OpenAI API credentials
openai.api_key = "YOUR_API_KEY_HERE"

# Connect to the drone
vehicle = dronekit.connect('udp:127.0.0.1:14550')

# Define drone control functions
def takeoff():
    vehicle.simple_takeoff(10)

def land():
    vehicle.mode = dronekit.VehicleMode("LAND")

def fly_to_object(object_id):
    # Implement zero-shot planning algorithm here
    pass

# Define image processing function
def process_image(image):
    # Implement object detection algorithm here
    return object_id, distance

# Define ChatGPT function
def chat(prompt):
    response = openai.Completion.create(
        engine="davinci",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.5
    )
    return response.choices[0].text.strip()

# Main loop
while True:
    # Get video feed from drone
    ret, frame = vehicle.get_video_stream()

    # Process image to detect objects
    object_id, distance = process_image(frame)

    # Prompt user for input
    prompt = f"What should the drone do? Fly to object {object_id} ({distance} meters away)?"
    response = chat(prompt)

    # Interpret user input and take appropriate action
    if "takeoff" in response:
        takeoff()
    elif "land" in response:
        land()
    elif "fly to object" in response:
        object_id = int(response.split("object ")[1].split(" ")[0])
        fly_to_object(object_id)

    # Wait for a short period of time to prevent overload
    time.sleep(0.1)

This program connects to a drone via the DroneKit API, processes the video feed to detect objects using OpenCV, prompts the user for input using OpenAI’s GPT-3 API, and takes appropriate action based on the user’s response. It also includes functions for drone control, image processing, and ChatGPT. This is just a simple example, but it shows how ChatGPT can be used to control drones and highlights the potential for this technology to revolutionize the field.

Conclusion

In this post, we explored how ChatGPT can be used to control drones by accessing object detection and distance data through APIs and leveraging special prompting structures, high-level APIs, and human feedback in text. We provided code snippets in Python for zero-shot planning, responding to dialogues, and seeking clarifications. By incorporating these techniques, we can improve the accuracy and efficiency of drone control systems and enable new applications of AI and NLP technologies.

Thank you for reading! I would love to hear from you and will do my best to respond promptly. Thank you again for your time, and have a great day! If you have any questions or feedback, please let us know in the comments below or email me.

Subscribe, follow and become a fan to get regular updates.