Simulation-setup-for-UAV-object-tracking

Simulation setup (raspberry pi) for UAV object tracking

first do this, then u can go on with the real raspberry pi 4

I’m gonna give you every little detail you need to set this up. You won’t have to guess, mess around, or get frustrated. I’ll explain everything step by step, like who’s got your back. Let’s dive in. I hope this small effort will benefit even a little bit..

Step 1: Simulating the Drone (Using SITL)

SITL (Software in the Loop) is like a pretend Pixhawk flight controller that runs on your computer. It lets you simulate how a drone flies, reads GPS, and reacts to commands.

1.1 Installing SITL

For Windows:

1. Download Mission Planner from this link.
2. Install it. That’s it—you’re good to go for SITL on Windows.

For Linux:

1. Open a terminal and run these commands:

   sudo apt update
   sudo apt install python3 python3-pip git -y
   git clone https://github.com/ArduPilot/ardupilot.git
   cd ardupilot
   git submodule update --init --recursive
   ./Tools/environment_install/install-prereqs-ubuntu.sh -y
   . ~/.profile
   ./waf configure --board sitl
   ./waf build

   This will download and set up SITL on your machine. It takes a little time, but don’t worry—this only happens once.

1.2 Launch SITL

To start the fake drone:

sim_vehicle.py -v ArduCopter --map --console

• --map: Opens a live map that shows where the drone is flying.
• --console: Opens a terminal where you can type commands for the drone.

If it launches, congrats! You’ve got a simulated drone.

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Step 2: Simulating the Raspberry Pi (Optional)

The Raspberry Pi part isn’t strictly necessary, but if you want to feel like you’re working with a Pi, you can set up a virtual Pi environment.

2.1 Using Docker for a Raspberry Pi Environment

Docker is like a sandbox where you can pretend you’re running a Raspberry Pi.

1. Download Docker from here.
2. Set it up by running this:

   docker pull balenalib/rpi-raspbian
   docker run -it balenalib/rpi-raspbian

   Boom—you’re inside a virtual Raspberry Pi!

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Step 3: Setting Up YOLOv7 (Object Detection)

Here’s where things get exciting. YOLOv7 (You Only Look Once) is an object detection algorithm that can identify stuff in images or videos—like cars, tanks, or even people.

3.1 Installing YOLOv7

1. Clone the YOLOv7 repository from GitHub:

   git clone https://github.com/WongKinYiu/yolov7.git
   cd yolov7

2. Install the required Python libraries:

   pip install -r requirements.txt

3.2 Running YOLOv7

1. Download the YOLOv7 weights (a pretrained model file). You can find it in the repo or from the YOLOv7 releases.
2. Use a sample video to test detection:

   python detect_and_track.py --source "test_video.mp4" --weights yolov7.pt --view-img

   • --source: Your video file (e.g., "test_video.mp4").
   • --weights: The YOLOv7 weight file (e.g., yolov7.pt).
   • --view-img: Lets you see the results as the video plays.

If it shows bounding boxes on objects in your video, it’s working! 🎉

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Step 4: Making the Drone and Camera Talk

Now, we need to link the object detection system (YOLOv7) with your fake drone (SITL). We’ll use Dronekit, a Python library, to send commands to the drone.

4.1 Install Dronekit

1. Install Dronekit and Dronekit SITL:

   pip install dronekit dronekit-sitl

4.2 Test the Drone Connection

1. Use this Python script to check if the drone is listening:

   from dronekit import connect
   
   # Connect to your simulated drone (default SITL address)
   vehicle = connect('127.0.0.1:14550', wait_ready=True)
   
   # Print the GPS coordinates
   print("GPS Location:", vehicle.location.global_frame)

2. Run the script. If it prints the GPS data, your drone is connected!

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Step 5: Simulating the Camera Feed

We’ll use pre-recorded videos or even simulate a webcam feed. Here’s how:

5.1 Pre-Recorded Videos

This is the easiest way. Just load a video file into YOLOv7 as the source:

python detect_and_track.py --source "test_video.mp4" --weights yolov7.pt --view-img

5.2 Simulate a Live Camera

Want to make it feel like you’re using a real camera? Let’s set that up.

For Linux:

1. Install v4l2loopback:

   sudo apt install v4l2loopback-dkms
   modprobe v4l2loopback

2. Stream a video to the virtual camera:

   ffmpeg -re -i test_video.mp4 -f v4l2 /dev/video0

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Step 6: Setting Up a Geofence (Stay Inside the Lines)

To stop the drone from wandering too far, we’ll use a geofence. Here’s how to code it:

from geopy.distance import geodesic

# Center of the geofence (lat, lon)
geofence_center = (37.7749, -122.4194)  # Example: San Francisco
geofence_radius = 500  # Radius in meters

# Simulated target location (lat, lon)
target_location = (37.7750, -122.4180)

# Check if the target is within the geofence
distance = geodesic(geofence_center, target_location).meters
if distance <= geofence_radius:
    print("Target is inside the geofence.")
else:
    print("Target is outside the geofence.")

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Step 7: Moving the Drone Toward a Target

Here’s how to send your drone to an object’s location:

from dronekit import LocationGlobal

# Target coordinates (lat, lon, altitude)
target_lat = 37.7750
target_lon = -122.4180
target_alt = 10  # Altitude in meters

# Command the drone to move
vehicle.simple_goto(LocationGlobal(target_lat, target_lon, target_alt))
print("Drone is moving to the target!")

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Step 8: Emergency Stop (Return to Launch)

If the drone leaves the geofence or something goes wrong, make it return to its starting point:

from dronekit import VehicleMode

# Change mode to "RTL" (Return to Launch)
vehicle.mode = VehicleMode("RTL")
print("Drone is returning to launch!")

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Final Step: Bring Everything Together

Now you’ve got:

1. SITL simulating the drone.
2. YOLOv7 spotting objects.
3. Dronekit sending commands to the drone.

Test the pipeline step by step:

1. Detect objects using YOLOv7.
2. Check if the object is in the geofence.
3. Command the drone to move to the object’s location.
4. If the object leaves the geofence, trigger Return to Launch.

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Quick Tips

• Use Mission Planner to monitor the drone in real-time.
• Always test one small step at a time—don’t try to run the whole thing at once.
• If you get stuck, drop me a message—I’m here for you.

You’ve got this! Let me know how it goes. 🚀

you know how to do that