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Road Following

In this example we'll collect an image regression dataset that will enable JetBot to follow a road! We'll teach JetBot to detect a target x, y image coordinate that the JetBot will chase. As JetBot gets closer to the point, it moves further along the track.

Step 1 - Collect data on JetBot

  1. Connect to your robot by navigating to http://<jetbot_ip_address>:8888
  2. Sign in with the default password jetbot
  3. Shutdown all other running notebooks by selecting Kernel -> Shutdown All Kernels...
  4. Navigate to ~/Notebooks/road_following/
  5. Open and follow the data_collection.ipynb notebook

Step 2 - Train neural network

Option 1 - Train on Jetson Nano
  1. Connect to your robot by navigating to http://<jetbot_ip_address>:8888
  2. Sign in with the default password jetbot
  3. In the Jupyter Lab tab, navigate to ~/Notebooks/road_following
  4. Open and follow the train_model.ipynb notebook
Option 2 - Train on other GPU machine

  1. Connect to a GPU machine with PyTorch installed and a Jupyter Lab server running

  2. Upload the road following avoidance training notebook to this machine

  3. Open and follow the train_model.ipynb notebook

Step 3 - Optimize the model on Jetson Nano

  1. Connect to your robot by navigating to https://<jetbot_ip_address>:8888
  2. Sign in with the default password jetbot
  3. Shutdown all other running notebooks by selecting Kernel -> Shutdown All Kernels...
  4. Navigate to ~/Notebooks/road_following
  5. Open and follow the live_demo_build_trt.ipynb notebook to optimize the model with TensorRT

Step 4 - Run live demo on JetBot

  1. Connect to your robot by navigating to http://<jetbot_ip_address>:8888
  2. Sign in with the default password jetbot
  3. Shutdown all other running notebooks by selecting Kernel -> Shutdown All Kernels...
  4. Navigate to ~/Notebooks/road_following
  5. Open and follow the live_demo_trt.ipynb notebook to run the optimized model
Warning

Start cautious and give JetBot enough space to move around.