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 detailed information on the FTL sample project, see the Getting started section.
The source code is released under Apache 2.0.
Follow these steps to install the AWS DeepRacer Follow the Leader (FTL) sample project.
The AWS DeepRacer device comes with all the prerequisite packages and libraries installed to run the FTL sample project.
The following are additional software and hardware requirements for using the FTL sample project on the AWS DeepRacer device.
-
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.
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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.
Open a terminal on the AWS DeepRacer device and run the following commands as the root user.
-
Switch to the root user before you source the ROS 2 installation:
sudo su -
Stop the
deepracer-core.servicethat is currently running on the device:systemctl stop deepracer-core -
Source the ROS 2 Foxy setup bash script:
source /opt/ros/foxy/setup.bash -
Set the environment variables required to run Intel OpenVino scripts:
source /opt/intel/openvino_2021/bin/setupvars.sh -
Create a workspace directory for the package:
mkdir -p ~/deepracer_ws cd ~/deepracer_ws -
Clone the entire FTL sample project on the AWS DeepRacer device:
git clone https://github.com/7Gao/aws-deepracer-follow-the-leader-sample-project.git -
Go to the work space:
cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ -
Clone the
async_web_server_cpp,web_video_server, andrplidar_ros dependencypackages on the AWS DeepRacer device:./install_dependencies.sh -
Fetch the unreleased dependencies:
rosws update -
Resolve the dependencies:
rosdep install -i --from-path . --rosdistro foxy -y -
Build the packages in the workspace:
colcon build
Follow this procedure to use the FTL sample application.
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.
-
Switch to the root user before you source the ROS 2 installation:
sudo su -
Stop the
deepracer-core.servicethat is currently running on the device:systemctl stop deepracer-core -
Source the ROS 2 Foxy setup bash script:
source /opt/ros/foxy/setup.bash -
Set the environment variables required to run Intel OpenVino scripts:
source /opt/intel/openvino_2021/bin/setupvars.sh -
Source the setup script for the installed packages:
source ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/install/setup.bash -
Launch the nodes required for the FTL sample project:
ros2 launch ftl_launcher ftl_launcher.py
Once the FTL sample application is launched, you can follow the steps here to open the AWS DeepRacer Vehicle's Device Console and checkout the FTL mode tab which will help you control the vehicle.
You can modify the MAX_SPEED scale of the AWS DeepRacer using an ROS 2 service call in case the car isn’t moving as expected. This can occur because of the vehicle battery percentage, the surface on which the car is operating, or for other reasons.
-
Switch to the root user before you source the ROS 2 installation:
sudo su -
Navigate to the FTL workspace:
cd ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/ -
Source the ROS 2 Foxy setup bash script:
source /opt/ros/foxy/setup.bash -
Source the setup script for the installed packages:
source ~/deepracer_ws/aws-deepracer-follow-the-leader-sample-project/deepracer_follow_the_leader_ws/install/setup.bash -
Change the
MAX SPEEDto xx% of theMAXScale:ros2 service call /ftl_navigation_pkg/set_max_speed deepracer_interfaces_pkg/srv/SetMaxSpeedSrv "{max_speed_pct: 0.xx}"Example: Change the
MAX SPEEDto 75% of theMAXScale:ros2 service call /ftl_navigation_pkg/set_max_speed deepracer_interfaces_pkg/srv/SetMaxSpeedSrv "{max_speed_pct: 0.75}"
The ftl_launcher.py, included in this package, is the main launcher file that launches all the required nodes for the FTL sample project. This launcher file also includes the nodes from the AWS DeepRacer core application.
from launch import LaunchDescription
from launch_ros.actions import Node
def generate_launch_description():
ld = LaunchDescription()
object_detection_node = Node(
package='object_detection_pkg',
namespace='object_detection_pkg',
executable='object_detection_node',
name='object_detection_node',
parameters=[{
'DEVICE': 'CPU',
'PUBLISH_DISPLAY_OUTPUT': True
}]
)
ftl_navigation_node = Node(
package='ftl_navigation_pkg',
namespace='ftl_navigation_pkg',
executable='ftl_navigation_node',
name='ftl_navigation_node'
)
camera_node = Node(
package='camera_pkg',
namespace='camera_pkg',
executable='camera_node',
name='camera_node',
parameters=[
{'resize_images': False}
]
)
ctrl_node = Node(
package='ctrl_pkg',
namespace='ctrl_pkg',
executable='ctrl_node',
name='ctrl_node'
)
deepracer_navigation_node = Node(
package='deepracer_navigation_pkg',
namespace='deepracer_navigation_pkg',
executable='deepracer_navigation_node',
name='deepracer_navigation_node'
)
software_update_node = Node(
package='deepracer_systems_pkg',
namespace='deepracer_systems_pkg',
executable='software_update_node',
name='software_update_node'
)
model_loader_node = Node(
package='deepracer_systems_pkg',
namespace='deepracer_systems_pkg',
executable='model_loader_node',
name='model_loader_node'
)
otg_control_node = Node(
package='deepracer_systems_pkg',
namespace='deepracer_systems_pkg',
executable='otg_control_node',
name='otg_control_node'
)
network_monitor_node = Node(
package='deepracer_systems_pkg',
namespace='deepracer_systems_pkg',
executable='network_monitor_node',
name='network_monitor_node'
)
deepracer_systems_scripts_node = Node(
package='deepracer_systems_pkg',
namespace='deepracer_systems_pkg',
executable='deepracer_systems_scripts_node',
name='deepracer_systems_scripts_node'
)
device_info_node = Node(
package='device_info_pkg',
namespace='device_info_pkg',
executable='device_info_node',
name='device_info_node'
)
battery_node = Node(
package='i2c_pkg',
namespace='i2c_pkg',
executable='battery_node',
name='battery_node'
)
inference_node = Node(
package='inference_pkg',
namespace='inference_pkg',
executable='inference_node',
name='inference_node'
)
model_optimizer_node = Node(
package='model_optimizer_pkg',
namespace='model_optimizer_pkg',
executable='model_optimizer_node',
name='model_optimizer_node'
)
rplidar_node = Node(
package='rplidar_ros2',
namespace='rplidar_ros',
executable='rplidar_scan_publisher',
name='rplidar_scan_publisher',
parameters=[{
'serial_port': '/dev/ttyUSB0',
'serial_baudrate': 115200,
'frame_id': 'laser',
'inverted': False,
'angle_compensate': True,
}]
)
sensor_fusion_node = Node(
package='sensor_fusion_pkg',
namespace='sensor_fusion_pkg',
executable='sensor_fusion_node',
name='sensor_fusion_node'
)
servo_node = Node(
package='servo_pkg',
namespace='servo_pkg',
executable='servo_node',
name='servo_node'
)
status_led_node = Node(
package='status_led_pkg',
namespace='status_led_pkg',
executable='status_led_node',
name='status_led_node'
)
usb_monitor_node = Node(
package='usb_monitor_pkg',
namespace='usb_monitor_pkg',
executable='usb_monitor_node',
name='usb_monitor_node'
)
webserver_publisher_node = Node(
package='webserver_pkg',
namespace='webserver_pkg',
executable='webserver_publisher_node',
name='webserver_publisher_node'
)
web_video_server_node = Node(
package='web_video_server',
namespace='web_video_server',
executable='web_video_server',
name='web_video_server'
)
ld.add_action(object_detection_node)
ld.add_action(ftl_navigation_node)
ld.add_action(camera_node)
ld.add_action(ctrl_node)
ld.add_action(deepracer_navigation_node)
ld.add_action(software_update_node)
ld.add_action(model_loader_node)
ld.add_action(otg_control_node)
ld.add_action(network_monitor_node)
ld.add_action(deepracer_systems_scripts_node)
ld.add_action(device_info_node)
ld.add_action(battery_node)
ld.add_action(inference_node)
ld.add_action(model_optimizer_node)
ld.add_action(rplidar_node)
ld.add_action(sensor_fusion_node)
ld.add_action(servo_node)
ld.add_action(status_led_node)
ld.add_action(usb_monitor_node)
ld.add_action(webserver_publisher_node)
ld.add_action(web_video_server_node)
return ld
| Parameter name | Description |
|---|---|
DEVICE (optional) |
If set as MYRIAD, uses the Intel Compute Stick 2 for inference. Else, uses the CPU for inference by default, even if it is removed. |
PUBLISH_DISPLAY_OUTPUT |
Set to True or False if the inference output images need to be published to localhost using web_video_server. |
resize_images |
Set to True or False depending on if you want to resize the images in camera_pkg |
