Integration with ROS 2¶

This chapter describes integration of Kenning with ROS 2 nodes and communication infrastructure.

Kenning can be used together with ROS 2 for:

• Evaluation of ROS 2 nodes and subsystems in terms of performance and quality

• Running AI models supported by Kenning and exposing topics/services for accessing them from ROS 2 nodes

• Delegating evaluation of models to remote target devices using ROS 2 communication

Running Kenning together with ROS 2¶

The easiest option to execute Kenning process in ROS 2 project is to use ROS 2 launch files providing kenning as an executable to run, with ros as a subcommand:

from launch_ros.actions import Node

# ...

kenning_node = Node(
        name="kenning_node",
        executable="kenning",
        arguments=["ros","flow","--verbosity","DEBUG"],
        parameters=[{
            "config_file":"./src/gui_node/examples/kenning-instance-segmentation/kenning-instance-segmentation.json"
        }]
    )

You can pass standard command line arguments like verbosity level using arguments parameters in Node. You can set different verbosity level for Kenning logger and ROS 2 logger.

If you want to see all logs for Kenning and ROS 2, set arguments to:

arguments=["--verbosity","DEBUG","--ros-args","--log-level","DEBUG"]

Setting Kenning parameters¶

You can use parameters section of Node to set all Kenning-related parameters.

To set Kenning pipeline you need to set appropriate arguments in Node:

arguments=["ros","optimize","test" ...

is equivalent to running Kenning command with:

kenning optimize test ...

To use scenario config file, config_file parameter is used to provide path to the standard Kenning’s scenario file:

"config_file":"./src/gui_node/examples/kenning-instance-segmentation/kenning-instance-segmentation.json"

But you can also provide every standard command line argument supported by Kenning, using ROS 2 parameters, for example:

arguments=["ros","optimize","test","--verbosity","DEBUG"],
parameters=[{
            "config_file":"./scripts/configs/tensorflow-pet-dataset-mobilenet.yml",
            "measurements":"./workspace/data.json",
            "report_path":"./report/report.md",
            "report_name":"Mobilenet Pet Dataset Test"
        }],

is equivalent to running the command:

kenning optimize test --cfg ./scripts/configs/tensorflow-pet-dataset-mobilenet.yml --measurements ./workspace/data.json --report-path ./report/report.md --report-name "Mobilenet Pet Dataset Test"

Preparing the Docker environment for ROS 2 integration¶

A sample Docker environment with all the necessary components is available in Dockerfile provided by example in repository ros2-gui-node.

The image contains:

• ROS 2 Humble environment

• OpenCV for image processing

• Apache TVM for model optimization and runtime

• CUDNN and CUDA libraries for NVIDIA GPU support

• Additional development tools

Pulling built image¶

The built image can be pulled with:

docker pull ghcr.io/antmicro/ros2-gui-node:kenning-ros2-demo

Building the image from scratch¶

First of clone ros2-gui-node repository:

git clone https://github.com/antmicro/ros2-gui-node
cd ros2-gui-node
git submodule update --init --recursive

Then go to the directory with example:

cd examples/kenning-instance-segmentation

then run the command:

docker build . --tag ghcr.io/antmicro/ros2-gui-node:kenning-ros2-demo