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Examples

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John edited this page Mar 14, 2019 · 67 revisions

This page lists the examples provided with JetBot.

Make sure your robot is connected to WiFi as described in the software setup

Example 1 - Basic Motion

In this example we'll control JetBot by programming from a web browser.

  1. Connect to your robot by navigating to http://<jetbot_ip_address>:8888

  2. Sign in with the default password jetbot

  3. Navigate to ~/Notebooks/basic_motion/

  4. Open and follow the basic_motion.ipynb notebook

    Make sure JetBot has enough space to move around.

Example 2 - Teleoperation

This example requires a gamepad controller connected to your workstation.

In this example we'll drive JetBot remotely, view live streaming video, and save snapshots!

  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/teleoperation/

  5. Open and follow the teleoperation.ipynb notebook

Example 3 - Collision avoidance

In this example we'll collect an image classification dataset that will be used to help keep JetBot safe! We'll teach JetBot to detect two scenarios free and blocked. We'll use this AI classifier to prevent JetBot from entering dangerous territory.

Step 1 - Collect data on JetBot

We provide a pre-trained model so you can skip to step 3 if desired.

  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/collision_avoidance/

  5. Open and follow the data_collection.ipynb notebook

Step 2 - Train neural network on cloud

  1. Navigate to https://courses.nvidia.com/dli-event in your web browser

  2. Enter the event code DLI_Jet_Demo

  3. Sign in to your NVIDIA Developer Account if you have not already

  4. Select View Course -> Course -> Click here to begin -> Start

  5. Wait a few minutes for the cloud training machine to set up

  6. Launch the Jupyter Lab by selecting Launch Task

  7. In the Jupyter Lab tab, navigate to ~/collision_avoidance

  8. Open and follow the train_model.ipynb notebook

Step 3 - Run live demo on JetBot

  1. Connect back 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/collision_avoidance

  5. Open and follow the live_demo.ipynb notebook

    Start cautious and give JetBot enough space to move around.

Video

This video shows multiple JetBots running collision avoidance

Example 4 - Object Following

In this example we'll have JetBot follow an object using a pre-trained model capable of detecting common objects likePerson, Cup, and Dog. While doing this, JetBot will run the collision avoidance model from Example 3 to make sure it stays safe!

  1. Connect to your robot by navigating to http://<jetbot_ip_address>:8888

  2. Shutdown all other running notebooks by selecting Kernel -> Shutdown All Kernels...

  3. Navigate to ~/Notebooks/object_following/

  4. Open and follow the live_demo.ipynb notebook

    Start cautious and give JetBot enough space to move around.

Video

This video shows JetBot following a person and avoiding obstacles

Next

Make JetBot smarter

  • Collect more collision avoidance data
  • Try out different neural network architectures (the torchvision package has lots!)
  • Modify the collision avoidance example for a new task (ie: cat / no cat. if cat then run)

Create something entirely new!

  • Modify the collision avoidance example for your own project
  • Try out some new hardware with Jetson Nano. It's easy with Jetson GPIO and Adafruit Blinka

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