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Run a ROS 2 Sample Application in the Docker* Container
Run an Intel® RealSense™ ROS 2 Sample Application in Docker* Container
Run a GStreamer* Video Pipeline using GStreamer* Plugins in Docker* Container
Run a GStreamer* Audio Pipeline using GStreamer* Plugins in Docker* Container
Run a GStreamer* Video Pipeline using libv4l2 in Docker* Container
Run a GStreamer* Video Pipeline using the Intel® RealSense™ Plugin in the Docker* Container
Optional (Only for Sony* IMX390 Setups): Run a GStreamer* Video Pipeline using Sony’s IMX390 MIPI Sensor in Docker* Container
Run OpenVINO™ Sample Applications in Docker* Container
Run ROS 2 OpenVINO™ Toolkit Sample Applications in Docker* Container
Run Intel® oneAPI Base Toolkit Sample Applications in Docker* Container
Run Profiling Application in Docker* Container with VTune™ Profiler
Run OpenVINO™ Benchmarking Tool
Run the ADBSCAN Algorithm
Launch Wandering Application with Gazebo* Simulation
Launch Wandering Application on AAEON* Robot Kit
Launch Cartographer with 2D LIDAR
Run FastMapping Algorithm
Run ROS 2 Navigation Sample Applications Using the ITS Path Planner Plugin in a Docker* Container
ITS Path Planner Plugin Customization
ITS Path Planner Plugin Parameters
Run the Edge Insights for Autonomous Mobile Robots Container in KVM Guest
Run a Collaborative SLAM System
Build New and Custom Docker* Images from the Edge Insights for Autonomous Mobile Robots SDK
Troubleshooting for Robot Tutorials
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ITS Path Planner Plugin Customization
The ROS 2 navigation bringup application is started using the turtlebot3 Gazebo* simulation, and it receives as input parameter its_nav2_params.yaml.
Check the code snippet from 01_docker_sdk_env/docker_compose/05_tutorials/its_path_planner.tutorial.yml:
export TURTLEBOT3_MODEL=waffle
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:/home/eiforamr/ros2_ws/install/turtlebot3_gazebo/share/turtlebot3_gazebo/models/
ros2 launch nav2_bringup tb3_simulation_launch.py params_file:=${CONTAINER_BASE_PATH}/01_docker_sdk_env/docker_compose/05_tutorials/param/its_nav2_params.yaml
To use the ITS path planner plugin, the following parameters are added in its_nav2_params.yaml:
planner_server:
ros__parameters:
expected_planner_frequency: 0.01
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "its_planner/ITSPlanner"
interpolation_resolution: 0.05
catmull_spline: False
smoothing_window: 15
buffer_size: 10
build_road_map_once: True
min_samples: 250
roadmap: "PROBABLISTIC"
w: 32
h: 32
n: 2
ITS Path Planner Plugin Parameters
catmull_spline:
If true, the generated path from the ITS is interpolated with the catmull spline method; otherwise, a smoothing filter is used to smooth the path.
smoothing_window:
The window size for the smoothing filter (The unit is the grid size.)
buffer_size:
During roadmap generation, the samples are generated away from obstacles. The buffer size dictates how far away from obstacles the roadmap samples should be.
build_road_map_once:
If true, the roadmap is loaded from the saved file; otherwise, a new roadmap is generated.
min_samples:
The minimum number of samples required to generate the roadmap
roadmap:
Either PROBABILISTIC or DETERMINISTIC
w:
The width of the window for intelligent sampling
h:
The height of the window for intelligent sampling
n:
The minimum number of samples that is required in an area defined by w and h
You can modify these values by editing the file below, at lines 251-267:
01_docker_sdk_env/docker_compose/05_tutorials/param/its_nav2_params.yaml