AgungHari/temp_Pengembangan-Kursi-Roda-Otonom-Berbasis-Yolov8-untuk-Penghindaran-Obstacle

Pendeteksian dengan menggabungkan dua pendekatan Yolo bounding box dan landmark pose, dimana kedua output ini dipetakan dalam grid 10x10 yang menjadi acuan kursi roda dalam menghindar. Perintah dikirim dari NUC ke ESP32 lalu menggerakan motor. Untuk lebih jelas buka Buku-Tugas-Akhir

Development of YOLOv8-based Autonomous Wheelchair for Obstacle Avoidance (Pengembangan Kursi Roda Otonom Berbasis YOLOv8 untuk Penghindaran Obstacle).

🚀 Detection is performed by combining two approaches: Yolo bounding box and pose landmarks, where both outputs are mapped into a 10x10 grid (made with OpenCV), which serves as a reference for the wheelchair to avoid obstacles. Commands are sent from the NUC to the ESP32, which then moves the motor.

🎬 Demo

🔨 Installation

PyPi version

I recommend a separate folder and venv for creating the model and the wheelchair program. for training venv setup :

  python --version
  python -m venv nama_venv
  nama_venv\Scripts\activate
  pip install ipykernel
  pip install ultralytics roboflow opencv-python

YOLOv8 need an absolute path. so change the data path for train, val, test in data.yaml example :

  names:
  -Manusia
  nc: 1
  roboflow:
    license: CC BY 4.0
    project: deteksi-manusia-yolov8-dataset
    url: https://universe.roboflow.com/hari-vijaya-kusuma/deteksi-manusia-yolov8-dataset/dataset/1
    version: 1
    workspace: hari-vijaya-kusuma
  test: D:/train yolo/Deteksi-Manusia-YoloV8-Dataset-1/test/images
  train: D:/train yolo/Deteksi-Manusia-YoloV8-Dataset-1/train/images
  val: D:/train yolo/Deteksi-Manusia-YoloV8-Dataset-1/valid/images

If you feel the training is taking too long, but you have a computer with NASA Super Computer like specs, it means you haven't utilized your GPU for the training process. check for cuda by running this

  import torch
  print(torch.cuda.is_available())

if the output is false then you need to install PyTorch with CUDA support. Check your driver version before implementing CUDA. example for installing CUDA 11.8

  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

You need an ESP32 and the wheelchair to run it.

  python --version
  python -m venv nama_venv
  nama_venv\Scripts\activate
  pip install mediapipe
  pip install ultralytics
  pip install opencv-python

🍿 Features

• Grid mapping that can map all detected humans.
• Optimized for use with GPU with the help of CUDA.
• Two avoidance options: human avoidance with a route returning to the main path and multiple human avoidance with a default avoidance route.

Authors

License