Blog #8 — AI & Robotics
- asmartiba4
- Apr 12, 2024
- 1 min read
Integrating AI deep learning libraries with ROS for data processing:
Data Collection: Gathering sensor data, images, and other information for training AI models.
Visual Data Processing: Preprocessing and analyzing visual data to extract features for AI algorithms.
Model Training: Training AI models using libraries like TensorFlow, PyTorch, or Keras for tasks such as object detection and prediction.
ROS Integration: Integrating AI models with ROS to enable intelligent tasks and interaction.
Digital Twinning
Creating a virtual representation of a physical object:
Data Collection: Gathering sensor data to create an accurate digital twin.
Data Integration & Processing: Processing data for accuracy and completeness in the model.
Model Simulation: Simulating the physical object's behavior using the digital twin.
ROS Bridge and Real-time Connection: Connecting the digital twin with ROS via rosbridge for real-time interaction.
3D Visualisation Tools: Using 3D visualization tools like rviz, ros3djs, and Gazebo.
Conclusions:
Project progress depends on the use case. I'm focused on researching the right tools and methods for implementation.
Challenges:
Exploring multiple directions with their own depths and use cases is challenging. Finding a balance between exploring and focusing on essential tools is crucial for effective progress.
