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