Advantech Robotic Suite/Robotic System/AFE-R360 AFE-R760/Quick Start

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Introduction

This page provides a Quick Start guide and practical tutorials specifically designed for the Autonomous Mobile Robot (AMR) domain. Whether you are new to ROS2 or an experienced developer, you can follow the clear step-by-step instructions to quickly get started and experience the rich features of the AMR software platform. Equipped with the Advantech Robotic Suite, the system is specially designed for AMR applications based on the ROS2 architecture, offering optimal solutions for smart logistics, automated warehousing, and related fields.

Tutorials 1: Explore Installed Robotic Node

Summary

In this tutorial, we will introduce how to use the Web UI to quickly access detailed hardware and software information in the system, including device info and ROS2 node list.

Step

  • Step 1: Open your browser and go to 127.0.0.1:8081
  • Step 2: Enter the default Password P@ssw0rd then login
Robotic-suite-quickstart-tutorials1_01.png
  • Step 3: Recommended to change the password first
Robotic-suite-quickstart-tutorials1_04.png

Robotic-suite-quickstart-tutorials1_05.png
  • Step 4: This page will display some system information
Robotic-suite-quickstart-tutorials1_02.png
  • Step 5: Next, select "ROS Nodes" from the list on the left

The vertical page will show open-source ROS2 nodes focused on the AMR field, for more information please refer to AMR SDK

Robotic-suite-quickstart-tutorials1_03.png

The vendor page will show a list of nodes from the Intel Robotics SDK, for more information please refer to Intel Robotic SDK 2.2

Robotic-suite-quickstart-tutorials1_06.png

Tutorials 2: Use Muilt Sensors on Time Sync Viewer

Summary

In this tutorial, We will publish fake sensor data, including camera, IMU, and LiDAR(you can also use actual sensors). Then display the timestamps in rqt and observe the time synchronization status of the multiple sensors.

For detailed instructions, please refer to Time Sync Viewer

Robotic-suite-quickstart-tutorials2_06.png

Tutorials 3: Show Camera and 3D Lidar Data on Rviz

Summary

In this tutorial, we will first download a sample rosbag that contains camera and 3D LiDAR data. Then, we will read and display these data in RViz, allowing users to visualize the contents of the sensor data.

Step

  • Step 1: Download example bagfile (About 1.4GB)
wget https://iedgeblob.blob.core.windows.net/iedge-public/rosbag-dataset/2011_09_26_drive_0039.tar.gz
tar zxvf 2011_09_26_drive_0039.tar.gz
  • Step 2: Play example bagfile in terminal
ros2 bag play 2011_09_26_drive_0039/ -l
  • Step 3: Run rviz2 in another terminal
rviz2
  • Step 4: Add image topic on Rviz
Robotic-suite-quickstart-tutorials3_01.png
  • Step 5: Add lidar topic on Rviz
Robotic-suite-quickstart-tutorials3_02.png
  • Step 6: Set parameters for correct display of pointcloud data
1. Set Fixed Frame to velodyne
2. Set Style to Points
3. Set Size (Pixels) to 2
  • Result:
Robotic-suite-quickstart-tutorials3_03.png

Tutorials 4: Create a 2D Map using SLAM

Summary

In this tutorial, we will demonstrate how to quickly use Cartographer to build a 2D map. We will first download a sample rosbag that contains Laser LiDAR data, and then show how to configure Cartographer’s parameters and start the mapping process. Finally, we will use RViz to observe the mapping progress and results in real time, allowing users to quickly get started with the 2D SLAM process and create their own maps.

For more information please refer to cartographer_ros

Step

  • Step 1: Download example bagfile
mkdir ~/slam_example
cd ~/slam_example
wget https://iedgeblob.blob.core.windows.net/iedge-public/rosbag-dataset/rosbag2_2023_08_04-10_54_23.tar.gz
tar zxvf rosbag2_2023_08_04-10_54_23.tar.gz
  • Step 2: Download lua file
cd ~/slam_example
wget -O 2d_slam_example.lua https://iedgeblob.blob.core.windows.net/iedge-public/robotic_suite_share_folder/2d_slam_example.lua
  • Step 3: Run SLAM node
cd ~/slam_example
ros2 launch cartographer_ros offline_backpack_2d.launch.py bag_filenames:=rosbag2_2023_08_04-10_54_23 configuration_directory:=./ configuration_basenames:=2d_slam_example.lua
  • Result:
Robotic-suite-quickstart-tutorials5_01.png

Tutorials 5: Run Object Detection with OpenVINO in Intel Robotics SDK

Summary

In this tutorial, you will learn how to easily run object detection using Intel OpenVINO(Intel OpenVINO is a powerful feature included in the Intel Robotics SDK), we will guide you through downloading a pre-trained model and launching the object detection program. For this tutorial, instead of using live camera data, we will use a rosbag file as the data source.

For more information please refer to ros2_openvino_toolkit

Step

  • Step 1: Download example bagfile (About 2.2GB)
wget https://iedgeblob.blob.core.windows.net/iedge-public/rosbag-dataset/103.tar.gz
tar zxvf 103.tar.gz
  • Step 2: Play example bagfile in terminal
ros2 bag play -l 103 --remap cam_front/raw:=/openvino_toolkit/image_raw
  • Step 3: Download ssdlite_mobilenet_v2 model
mkdir /opt/ros/humble/share/openvino_node/model
cd /opt/ros/humble/share/openvino_node/model
wget https://iedgeblob.blob.core.windows.net/iedge-public/share-folder/ssdlite_mobilenet_v2.tar.gz
tar zxvf ssdlite_mobilenet_v2.tar.gz
  • Step 4: Download openvino_node param

Download pipeline_object_topic.yaml 

cd /opt/ros/humble/share/openvino_node/param
wget -O pipeline_object_topic.yaml https://iedgeblob.blob.core.windows.net/iedge-public/robotic_suite_share_folder/pipeline_object_topic.yaml
  • Step 5: Run openvino_node in another terminal
ros2 launch openvino_node pipeline_object_topic.launch.py
  • Result:
Robotic-suite-quickstart-tutorials4_01.png


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