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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
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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Basic Fleet Management
The basic fleet management solution consists of server and client architecture. The following diagram presents the architecture, components, and communication between components.
Basic Fleet Management use cases are triggered by battery level:
Basic Fleet Management use case, commanding a robot to return to a docking station (See the Get Started Guide for Robot Orchestration.)
Remote Inference use case with a remote inference request to an OpenVINO™ model server
The basic fleet management server is one of the microservices of orchestration, and it is the solution provided by ThingsBoard* (https://thingsboard.io/docs/user-guide/install/docker/).
Using a ThingsBoard* server to connect to the Intel® In-Band Manageability framework (https://github.com/intel/intel-inb-manageability), deployed clients are able to provide fleet management and telemetry. The ThingsBoard* server GUI gives a clear view of the telemetry data with the Intel® In-Band Manageability-tailored dashboard. In addition, rules can be set for configured, validated events to reach fleet management use cases.
The basic fleet management client (which is deployed on robots) consists of Intel® In-Band Manageability, the VDA5050 sample handler, and ROS 2 nodes (can be navigation or object detection purposes). When a subscribed topic is published by Intel® In-Band Manageability, the VDA5050 sample handler processes the VDA5050 complied JSON format and translates it into ROS2 topics to publish.
The VDA5050 complied JSON format message can be conducted in the ThingsBoard* Rule Engine (https://thingsboard.io/docs/user-guide/rule-engine-2-0/re-getting-started/) nodes with configured telemetry message validation and sent via an RPC call node, or it can be sent manually on the GUI.
For the remote inference use case, the requests from ROS 2 node go to OpenVINO™ model server (https://github.com/openvinotoolkit/model_server/tree/main/extras/nginx-mtls-auth) via SSL channel.