[DOCS][Metro][Robot] Link fixes

wiwaszko-intel · wiwaszko-intel · commit 8b0dcf86dcc2 · 2026-03-16T13:30:36.000+01:00
diff --git a/metro-ai-suite/deterministic-threat-detection/docs/user-guide/get-started.md b/metro-ai-suite/deterministic-threat-detection/docs/user-guide/get-started.md
@@ -16,7 +16,7 @@ system in action.
 
 The experimental setup consists of:
 
-- **2 × [AXIS RTSP Camera P3265-LVE](https://www.axis.com/products/axis-p3265-lve)**
+- **2 × [AXIS RTSP Camera P3265-LVE](https://www.axis.com/products/axis-p3265-lve/support)**
 - **1 × [Moxa Managed Switch TSN-G5000 Series](https://www.moxa.com/getmedia/a0db0ef9-2741-4bad-91c6-1ec1827aca64/moxa-tsn-g5000-series-web-console-manual-v2.3.pdf)**
 - **5 × Arrow Lake Linux Machines with `Intel i226` TSN network cards**
 
diff --git a/metro-ai-suite/live-video-analysis/live-video-search/docs/user-guide/how-it-works.md b/metro-ai-suite/live-video-analysis/live-video-search/docs/user-guide/how-it-works.md
@@ -34,4 +34,4 @@ graph TD
 ## Related Architecture References
 
 - Smart NVR architecture: [Smart NVR Overview](../../../../smart-nvr/docs/user-guide/index.md)
-- VSS Search architecture: [Video Search and Summarization Docs](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/video-search-and-summarization/docs/user-guide/overview-architecture-search.md)
+- VSS Search architecture: [Video Search and Summarization Docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/video-search-and-summarization/how-it-works/video-search-and-summarization.html)
diff --git a/metro-ai-suite/live-video-analysis/live-video-search/docs/user-guide/how-to-build-from-source.md b/metro-ai-suite/live-video-analysis/live-video-search/docs/user-guide/how-to-build-from-source.md
@@ -4,8 +4,8 @@ Live Video Search depends on **VSS Search** and **Smart NVR** images. Follow the
 
 ## Build VSS Images
 
-- VSS build guide (public):
- [How to Build from Source](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/video-search-and-summarization/docs/user-guide/build-from-source.md)
+- VSS build guide:
+ [How to Build from Source](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/video-search-and-summarization/docs/user-guide/build-from-source.md)
 
 ## Build Smart NVR Images
 
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/get-started.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/get-started.md
@@ -156,7 +156,7 @@ Profiling and monitoring performance of Metro Vision AI workloads using command-
 
 ### Technical Documentation
 
-- [DL Streamer](http://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/index.html)
+- [DL Streamer](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/index.html)
   \- Comprehensive documentation for Intel's GStreamer-based video analytics framework
 - [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
   \- RESTful microservice architecture documentation for scalable video analytics deployment
diff --git a/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-3.md b/metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/tutorial-3.md
@@ -121,4 +121,4 @@ After completing this tutorial, you should have:
 
 - [Grafana HTML Panel Documentation](https://grafana.com/docs/grafana/latest/visualizations/panels-visualizations/visualizations/)
 - [MQTT Data Source Configuration](https://grafana.com/docs/grafana/latest/datasources/)
-- [Dashboard Best Practices](https://grafana.com/docs/grafana/latest/best-practices/)
+- [Grafana Dashboard Best Practices](https://grafana.com/docs/grafana/latest/visualizations/dashboards/build-dashboards/best-practices/)
diff --git a/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/advanced-user-guide.md b/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/advanced-user-guide.md
@@ -1081,5 +1081,5 @@ Follow the guide from [Get Started](./get-started-guide.md) to run sensor fusion
 Some of the code is referenced from the following projects:
 
 - [IGT GPU Tools](https://gitlab.freedesktop.org/drm/igt-gpu-tools)(MIT License)
-- [Intel DL Streamer](https://github.com/dlstreamer/dlstreamer) (MIT License)
+- [Intel DL Streamer](https://github.com/open-edge-platform/dlstreamer) (MIT License)
 - [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) (Apache-2.0 License)
diff --git a/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/get-started-guide/prerequisites.md b/metro-ai-suite/sensor-fusion-for-traffic-management/docs/user-guide/get-started-guide/prerequisites.md
@@ -1,55 +1,52 @@
-#	Prerequisites and Dependencies
+# Prerequisites and Dependencies
 
 ## Prerequisites
 
 - Operating System
-
-    - [Ubuntu 24.04.2 Desktop LTS](https://old-releases.ubuntu.com/releases/24.04.2/ubuntu-24.04.2-desktop-amd64.iso) (fresh installation) on Intel® Core™ Ultra 7 265H platform
-    -  [Ubuntu 24.10](https://releases.ubuntu.com/24.10/ubuntu-24.10-desktop-amd64.iso) (fresh installation) on Intel® Core™ i7-13700 and Intel® B580 Graphics platform
+  - [Ubuntu 24.04.2 Desktop LTS](https://old-releases.ubuntu.com/releases/24.04.2/ubuntu-24.04.2-desktop-amd64.iso) (fresh installation) on Intel® Core™ Ultra 7 265H platform
+  - [Ubuntu 24.10](https://old-releases.ubuntu.com/releases/24.10/ubuntu-24.10-desktop-amd64.iso) (fresh installation) on Intel® Core™ i7-13700 and Intel® B580 Graphics platform
 
 - Platform
-
-    - Intel® Core™ Ultra 7 265H + Ubuntu24.04 (2C+1L/4C+2L usecase)
-    - Intel® Core™ i7-13700 and Intel® B580 Graphics + Ubuntu24.10 (8C+4L/12C+2L/12C4L usecase)
+  - Intel® Core™ Ultra 7 265H + Ubuntu24.04 (2C+1L/4C+2L usecase)
+  - Intel® Core™ i7-13700 and Intel® B580 Graphics + Ubuntu24.10 (8C+4L/12C+2L/12C4L usecase)
 
 - Intel® OpenVINO™ Toolkit
-
-    - Version Type: 2025.2
+  - Version Type: 2025.2
 
 - Ensure that proxy settings are configured if target system is within proxy environment
 
-    ```bash
-    export http_proxy=<Your-Proxy>
-    export https_proxy=<Your-Proxy>
-    ```
+  ```bash
+  export http_proxy=<Your-Proxy>
+  export https_proxy=<Your-Proxy>
+  ```
 
-    ```bash
-    sudo vim /etc/environment
-    # set proxy in /etc/environment
-    # http_proxy=<Your-Proxy>
-    # https_proxy=<Your-Proxy>
-    ```
+  ```bash
+  sudo vim /etc/environment
+  # set proxy in /etc/environment
+  # http_proxy=<Your-Proxy>
+  # https_proxy=<Your-Proxy>
+  ```
 
 ### Modules
 
--   AI Inference Service:
+- AI Inference Service:
+  - Media Processing (Camera)
+  - Radar Processing (mmWave Radar)
+  - Lidar Processing
+  - Sensor Fusion
 
-    -   Media Processing (Camera)
-    -   Radar Processing (mmWave Radar)
-    -   Lidar Processing
-    -   Sensor Fusion
-
--   Demo Application
+- Demo Application
 
 #### AI Inference Service
 
 AI Inference Service is based on the HVA pipeline framework. In this SW RI, it includes the functions of DL inference, radar signal processing, and data fusion.
 
 AI Inference Service exposes both RESTful API and gRPC API to clients, so that a pipeline defined and requested by a client can be run within this service.
 
--   RESTful API: listens to port 50021
+- RESTful API: listens to port 50021
+
+- gRPC API: listens to port 50052
 
--   gRPC API: listens to port 50052
 ```bash
 vim $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config
 ...
@@ -59,16 +56,16 @@ RESTfulPort=50021
 gRPCPort=50052
 ```
 
-
 #### Demo Application
+
 ![Demo-2C1L](../_assets/Demo-2C1L.png)
+
 <center>Figure 1. Visualization of 2C+1L results</center>
 
 Currently we support four display types: media, lidar, media_lidar, media_fusion.
 
 Make sure you meet the [system requirements](./system-req.md).
 
-
 ## Install Dependencies and Build Project
 
 - install driver related libs
@@ -79,7 +76,7 @@ Make sure you meet the [system requirements](./system-req.md).
   bash install_driver_related_libs.sh
   ```
 
-  Note that this step may restart the machine several times. Please rerun this script after each restart until you see the output of `All driver libs installed successfully`.
+> **Note:** this step may restart the machine several times. Please rerun this script after each restart until you see the output of `All driver libs installed successfully`.
 
 - install project related libs
 
@@ -90,26 +87,34 @@ Make sure you meet the [system requirements](./system-req.md).
   ```
 
 - set $PROJ_DIR
+
   ```bash
   cd metro-ai-suite/sensor-fusion-for-traffic-management
   export PROJ_DIR=$PWD
   ```
+
 - build liblidar.so
+
   ```bash
   cd $PROJ_DIR/ai_inference/liblidar
   rm liblidar.so
   bash build.sh
   ```
+
 - prepare global radar configs in folder: /opt/datasets
-    ```bash
-    sudo ln -s $PROJ_DIR/ai_inference/deployment/datasets /opt/datasets
-    ```
+
+  ```bash
+  sudo ln -s $PROJ_DIR/ai_inference/deployment/datasets /opt/datasets
+  ```
 
 - prepare models in folder: /opt/models
-    ```bash
-    sudo ln -s $PROJ_DIR/ai_inference/deployment/models /opt/models
-    ```
+
+  ```bash
+  sudo ln -s $PROJ_DIR/ai_inference/deployment/models /opt/models
+  ```
+
 - build project
-    ```bash
-    bash -x build.sh
-    ```
+
+  ```bash
+  bash -x build.sh
+  ```
diff --git a/metro-ai-suite/smart-nvr/docs/user-guide/get-started/system-requirements.md b/metro-ai-suite/smart-nvr/docs/user-guide/get-started/system-requirements.md
@@ -7,7 +7,7 @@ This page provides detailed hardware, software, and platform requirements to hel
 The base requirements for Smart NVR is dependent on the respective video analytics pipeline being utilized. Below is the list of requirements as mapped to each constituent components of the Smart NVR application
 
 - Frigate NVR: Refer to Frigate documentation, specifically the section that maps to
-  [OpenVINO](https://docs.frigate.video/frigate/hardware#openvino).
+  [OpenVINO](https://docs.frigate.video/frigate/hardware/#openvino---intel).
 - Video Search and Summary: Refer to the
   [system requirements](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/video-search-and-summarization/docs/user-guide/get-started/system-requirements.md)
   page of the sample application.
diff --git a/metro-ai-suite/smart-route-planning-agent/docs/user-guide/get-started.md b/metro-ai-suite/smart-route-planning-agent/docs/user-guide/get-started.md
@@ -91,7 +91,7 @@ Agent and multiple Smart Traffic Intersection Agent edge nodes.
 
 ### Multi-Node Deployment Prerequisites
 
-1. Deploy the [Smart Traffic Intersection Agent](https://github.com/open-edge-platform/edge-ai-suites/blob/main/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started.md#quick-start-with-setup-script) on each edge node.
+1. Deploy the [Smart Traffic Intersection Agent](../../../smart-traffic-intersection-agent/docs/user-guide/get-started.md#quick-start-with-setup-script) on each edge node.
 2. Ensure network connectivity between the central node and edge nodes.
 3. Note the IP address and port of each Smart Traffic Intersection Agent.
 
diff --git a/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started/deploy-with-helm.md b/metro-ai-suite/smart-traffic-intersection-agent/docs/user-guide/get-started/deploy-with-helm.md
@@ -414,6 +414,6 @@ helm install stia . -n traffic -f values-override.yaml \
 
 ## Related Links
 
-- [Get Started](./get-started.md)
-- [API Reference](./api-reference.md)
-- [Release Notes](./release-notes.md)
+- [Get Started](../get-started.md)
+- [API Reference](../api-reference.md)
+- [Release Notes](../release-notes.md)
diff --git a/metro-ai-suite/video-processing-for-nvr/svet2/svet_app/docker/README.md b/metro-ai-suite/video-processing-for-nvr/svet2/svet_app/docker/README.md
@@ -8,25 +8,32 @@
 **Software:**
 * VPP SDK
 
-## Build docker image  
-1. Build docker image for reference application `bash build_sample.sh`  
-Make sure docker is corrently installed and configured. 
+## Build docker image
+
+1. Build docker image for reference application `bash build_sample.sh`
+Make sure docker is corrently installed and configured.
+
+## Run docker container
 
-## Run docker container  
 1. Run `sudo init 3` switch to non-GUI mode
-2. Run a sample test in docker container : `bash run.sh`  
-Note: Make sure there is at least one 1080p/4K display connected to the device and switch to root and `init 3` before running the command. You can follow [get-started-guide](../docs/user-guide/get-started-guide.md) and run `svet_app load  sample_config/basic/show_displays.txt` to check how many displays are connected and the display resolution
+2. Run a sample test in docker container : `bash run.sh`
+
+Note: Make sure there is at least one 1080p/4K display connected to the device and switch to root and `init 3` before running the command. You can follow [get-started-guide](../../../docs/user-guide/get-started-guide.md) and run `svet_app load  sample_config/basic/show_displays.txt` to check how many displays are connected and the display resolution
 
 ## Run docker compose
+
 1. Run `sudo init 3` switch to non-GUI mode
 2. Run `bash ./startup.sh`
 
 ## Uninstall docker image
+
 1. Run `docker rmi -f $(docker images --format "{{.Repository}}:{{.Tag}}" | grep 'vppsample')` remove all vppsample docker images
 
 ## Old platform compatible
-The configuration in docker script support MTL/ARL platform by default.  
+
+The configuration in docker script support MTL/ARL platform by default.
 To run on old platform like ADL/RPL, please remove environment variable `-e DISPLAY_NEW_PLATFORM=1` in `run.sh` and `DISPLAY_NEW_PLATFORM: "1"` in `docker-compose.yml`.
 
 ## Caution
+
 This container image is intended for demo purposes only and not intended for production use. To receive expanded security maintenance from Canonical on the Ubuntu base layer, you may follow the [how-to guide to enable Ubuntu Pro in a Dockerfile](https://documentation.ubuntu.com/pro-client/en/docs/howtoguides/enable_in_dockerfile) which will require the image to be rebuilt.
diff --git a/robotics-ai-suite/docs/robotics/dev_guide/tutorials_amr/developer_kit/robot-keyboard-teleop.md b/robotics-ai-suite/docs/robotics/dev_guide/tutorials_amr/developer_kit/robot-keyboard-teleop.md
@@ -13,7 +13,7 @@ You have a robot and a keyboard or an SSH/VNC connection to the robot.
 1. Connect to your robot via SSH/VNC or direct access. If you choose direct
    access, insert a monitor and a keyboard into the robot's compute system.
 
-2. Set up your system by following the steps in the [Prepare System](../../../gsg_robot/prepare-system.md) guide.
+2. Set up your system by following the steps in the [Prepare System](../../../gsg_robot/index.md) guide.
 
 3. Ensure the `ros2-amr-interface` Deb package is installed:
 
diff --git a/robotics-ai-suite/docs/robotics/dev_guide/tutorials_amr/navigation/follow_me/Simulation_demos/followme-with-gesture-audio.md b/robotics-ai-suite/docs/robotics/dev_guide/tutorials_amr/navigation/follow_me/Simulation_demos/followme-with-gesture-audio.md
@@ -98,7 +98,7 @@ pip3 install -r /opt/ros/humble/share/followme_turtlebot3_gazebo/scripts/require
 
 Please make sure to source the `/opt/ros/jazzy/setup.bash` file at first before
 executing any command in a new terminal. You can get more details in
-[Prepare the Target System](../../../../../gsg_robot/prepare-system.md) page.
+[Prepare the Target System](../../../../../gsg_robot/index.md) page.
 
 Run the following script to launch Gazebo simulator and ROS 2 rviz2.
 
@@ -182,7 +182,7 @@ the `start` and `stop` activity of the TurtleBot3 robot.
 
 Please make sure to source the `/opt/ros/jazzy/setup.bash` file (or `/opt/ros/humble/setup.bash`)
 at first before executing any command in a new terminal.
-You can get more details in [Prepare the Target System](../../../../../gsg_robot/prepare-system.md) page.
+You can get more details in [Prepare the Target System](../../../../../gsg_robot/index.md) page.
 
 Run the following script to launch Gazebo simulator and ROS 2 rviz2.
 
@@ -311,7 +311,7 @@ Find a brief description of the parameters in the following list:
 
 - Please make sure to prepare your environment before executing ROS 2 commands in
   a new terminal. You can find the instructions in the
-  [Prepare the Target System](../../../../../gsg_robot/prepare-system.md) page.
+  [Prepare the Target System](../../../../../gsg_robot/index.md) page.
 
 - You can stop the demo anytime by pressing `ctrl-C`. If the `Gazebo` simulator
   freezes or does not stop, please use the following command in a terminal:
