No version for distro humble showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange

No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.

Package Summary

Tags No category tags.
Version 0.0.1
License Apache 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Learn to deploy an object detection model that enables the AWS DeepRacer device to identify and follow an object.
Checkout URI https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
VCS Type git
VCS Version main
Last Updated 2022-06-06
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

This package contains launcher file to launch the nodes required for follow-the-leader application.

Additional Links

No additional links.

Maintainers

  • AWS DeepRacer

Authors

No additional authors.

AWS DeepRacer Follow the Leader (FTL) launcher package

Overview

The AWS DeepRacer Follow the Leader (FTL) sample project is an sample application built on top of the existing AWS DeepRacer application, which uses an object-detection machine learning model through which the AWS DeepRacer device can identify and follow a person. For information, see Getting Started.

License

The source code is released under Apache 2.0.

Installation

Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) launcher package.

Prerequisites

The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project. For more information about the preinstalled set of packages and libraries on the AWS DeepRacer, and about installing the required build systems, see Getting started. The FTL sample project requires the AWS DeepRacer application to be installed on the device, because it leverages most of the packages from the core application.

The following are the additional software and hardware requirements to get the FTL sample project to work on the AWS DeepRacer device.

  1. Download and optimize the object-detection model: Follow the instructions to download and optimize the object-detection model and copy it to the required location on the AWS DeepRacer device.

  2. Calibrate the AWS DeepRacer (optional): Follow the instructions to calibrate the mechanics of your AWS DeepRacer vehicle so the vehicle performance is optimal and it behaves as expected.

  3. Set up the Intel Neural Compute Stick 2 (optional): The object_detection_node provides functionality to offload the inference to a Intel Neural Compute Stick 2 connected to the AWS DeepRacer device. This is an optional setting that enhances the inference performance of the object-detection model. For more details about running inference on the Movidius NCS (Neural Compute Stick) with OpenVINO™ toolkit, see this video.

Attach the Neural Compute Stick 2 firmly in the back slot of the AWS DeepRacer, open a terminal, and run the following commands as the root user to install the dependencies of the Intel Neural Compute Stick 2 on the AWS DeepRacer device.

  1. Switch to the root user:

         sudo su
    
  2. Navigate to the OpenVino installation directory:

         cd /opt/intel/openvino_2021/install_dependencies
    
  3. Set the environment variables required to run the Intel OpenVino scripts:

         source /opt/intel/openvino_2021/bin/setupvars.sh
    
  4. Run the dependency installation script for the Intel Neural Compute Stick:

         ./install_NCS_udev_rules.sh
    

Downloading and Building

Open a terminal on the AWS DeepRacer device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

     sudo su
    
  2. Stop the deepracer-core.service that is currently running on the device:

     systemctl stop deepracer-core
    
  3. Source the ROS 2 Foxy setup bash script:

     source /opt/ros/foxy/setup.bash 
    
  4. Set the environment variables required to run Intel OpenVino scripts:

     source /opt/intel/openvino_2021/bin/setupvars.sh
    
  5. Create a workspace directory for the package:

     mkdir -p ~/deepracer_ws
     cd ~/deepracer_ws
    
  6. Clone the entire FTL sample project on the AWS DeepRacer device:

     git clone https://github.com/aws-deepracer/aws-deepracer-follow-the-leader-sample-project.git
     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
    
  7. Clone the async_web_server_cpp, web_video_server, and rplidar_ros dependency packages on the AWS DeepRacer device:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && ./install_dependencies.sh
    
  8. Fetch the unreleased dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/
     rosws update
    
  9. Resolve the dependencies:

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && rosdep install -i --from-path . --rosdistro foxy -y
    
  10. Build the packages in the workspace

     cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ && colcon build
    

Using the FTL sample application

Follow this procedure to use the FTL sample application.

Running the node

To launch the FTL sample application as the root user on the AWS DeepRacer device, open another terminal on the device and run the following commands as the root user.

  1. Switch to the root user before you source the ROS 2 installation:

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Package Dependencies

Deps Name
ament_cmake
ament_lint_auto
ament_lint_common
camera_pkg
ctrl_pkg
deepracer_navigation_pkg
deepracer_systems_pkg
device_info_pkg
i2c_pkg
inference_pkg
model_optimizer_pkg
rplidar_ros
sensor_fusion_pkg
servo_pkg
status_led_pkg
usb_monitor_pkg
webserver_pkg
web_video_server

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged ftl_launcher at Robotics Stack Exchange