Difference between revisions of "Advantech Robotic Suite/Robotic System/AMR SDK"
From ESS-WIKI
| (9 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| − | + | __NOTOC__ | |
| + | = Introduction = | ||
| + | <big>Advantech Robotic Suite offers five main categories of ROS2 services specifically designed for AMR applications, helping users accelerate development. These services include a variety of applications, algorithms and reference documentation to support efficient integration and deployment.</big> | ||
| − | + | == Planning == | |
| − | |||
| − | = Planning = | ||
<div style="overflow-x:auto;"> | <div style="overflow-x:auto;"> | ||
{| class="wikitable" style="width:90%;" | {| class="wikitable" style="width:90%;" | ||
| Line 11: | Line 11: | ||
|- | |- | ||
| Waypoint Following | | Waypoint Following | ||
| − | | | + | | Within Nav2, waypoint following is a feature that allows a robot to autonomously navigate through a series of specified points (waypoints) in sequence. It leverages the navigation stack’s planning and control capabilities to ensure accurate and reliable traversal of a given route. |
| − | | | + | | [https://github.com/ros-navigation/navigation2/tree/main/nav2_waypoint_follower Doc] |
|- | |- | ||
| Path Planning | | Path Planning | ||
| − | | | + | | Path planning in Nav2 refers to the process where the navigation stack computes a feasible and efficient path from the robot’s current location to its target destination. This process considers the environment’s obstacles and robot’s kinematics, using built-in planning algorithms. |
| − | | | + | | [https://github.com/ros-navigation/navigation2/tree/main/nav2_planner Doc] |
|} | |} | ||
</div> | </div> | ||
| − | = Visual Perception = | + | == Visual Perception == |
<div style="overflow-x:auto;"> | <div style="overflow-x:auto;"> | ||
{| class="wikitable" style="width:90%;" | {| class="wikitable" style="width:90%;" | ||
| Line 28: | Line 28: | ||
|- | |- | ||
| Barcode Recognition | | Barcode Recognition | ||
| − | | Basic | + | | Basic ROS2 wrapper for the zbar barcode reader library. Reads image stream from image topic, and outputs detected barcodes to barcode topic. Works with 1D and 2D barcodes. |
| [https://github.com/ros-drivers/zbar_ros Github] | | [https://github.com/ros-drivers/zbar_ros Github] | ||
|} | |} | ||
</div> | </div> | ||
| − | = Sensing Perception = | + | == Sensing Perception == |
<div style="overflow-x:auto;"> | <div style="overflow-x:auto;"> | ||
{| class="wikitable" style="width:90%;" | {| class="wikitable" style="width:90%;" | ||
| Line 41: | Line 41: | ||
|- | |- | ||
| IMU Tools | | IMU Tools | ||
| − | | | + | | A set of utilities for processing and visualizing data from Inertial Measurement Units (IMUs), which provide orientation, acceleration, and angular velocity information. |
| − | | | + | | [https://github.com/CCNYRoboticsLab/imu_tools Github] |
|} | |} | ||
</div> | </div> | ||
| − | = SLAM = | + | == SLAM == |
<div style="overflow-x:auto;"> | <div style="overflow-x:auto;"> | ||
{| class="wikitable" style="width:90%;" | {| class="wikitable" style="width:90%;" | ||
| Line 54: | Line 54: | ||
|- | |- | ||
| RTAB-Map | | RTAB-Map | ||
| − | | | + | | Real-Time Appearance-Based Mapping (RTAB-Map) is a graph-based SLAM (Simultaneous Localization and Mapping) algorithm that creates 3D maps using visual, depth, and sensor data. |
| − | | | + | | [https://github.com/introlab/rtabmap_ros Github] |
|- | |- | ||
| Cartographer | | Cartographer | ||
| − | | | + | | A real-time SLAM algorithm developed by Google that enables robots to build 2D and 3D maps of their environment using laser and odometry data. |
| − | | | + | | [https://google-cartographer-ros.readthedocs.io/en/latest/ Doc]<br>[https://github.com/ros2/cartographer_ros Github] |
|- | |- | ||
| SLAM Toolbox | | SLAM Toolbox | ||
| − | | | + | | A collection of SLAM algorithms and tools for mapping and localization, supporting online and offline map building, pose-graph optimization, and loop closure. |
| − | | | + | | [https://github.com/SteveMacenski/slam_toolbox Github] |
|- | |- | ||
| LIO-SAM | | LIO-SAM | ||
| − | | | + | | Lidar-Inertial Odometry via Smoothing and Mapping (LIO-SAM) is a state-of-the-art SLAM system that fuses LiDAR and IMU data to produce accurate, high-frequency odometry and maps. |
| − | | | + | | [https://github.com/TixiaoShan/LIO-SAM/tree/ros2 Github] |
|} | |} | ||
</div> | </div> | ||
| − | = Navigation /Localization = | + | == Navigation /Localization == |
<div style="overflow-x:auto;"> | <div style="overflow-x:auto;"> | ||
{| class="wikitable" style="width:90%;" | {| class="wikitable" style="width:90%;" | ||
| Line 79: | Line 79: | ||
|- | |- | ||
| Navigation 2 | | Navigation 2 | ||
| − | | | + | | A ROS2 framework that provides a complete set of navigation features, including localization, path planning, and control, for autonomous robot movement in dynamic environments. |
| − | | | + | | [https://docs.nav2.org/ Doc]<br>[https://github.com/ros-navigation/navigation2 Github] |
|- | |- | ||
| Robot Localization | | Robot Localization | ||
| − | | | + | | A ROS2 package that fuses data from various sensors (e.g., GPS, IMU, odometry) to provide accurate state estimation of a robot’s position and orientation. |
| − | | | + | | [https://docs.ros.org/en/melodic/api/robot_localization/html/index.html Doc]<br>[https://github.com/cra-ros-pkg/robot_localization Github] |
|- | |- | ||
| Hybrid A* | | Hybrid A* | ||
| − | | | + | | Hybrid A* is one of the path planning algorithms integrated in Nav2. It enhances traditional A* by considering the robot's motion constraints and continuous state space, making it suitable for robots that require smooth and realistic paths, especially non-holonomic vehicles. |
| − | | | + | | [https://github.com/ros-navigation/navigation2/tree/main/nav2_navfn_planner Doc] |
|- | |- | ||
| Dijkstra | | Dijkstra | ||
| − | | | + | | Dijkstra’s algorithm, available within Nav2’s planning plugins, is used to find the shortest path in a grid map from start to goal. It guarantees the optimal solution and serves as a robust global planner for route generation in the navigation stack. |
| − | | | + | | [https://github.com/ros-navigation/navigation2/tree/main/nav2_navfn_planner Doc] |
|- | |- | ||
| Obstacle Avoidance | | Obstacle Avoidance | ||
| − | | | + | | Obstacle avoidance in Nav2 is handled by its local planner and costmap modules. These components work together to detect obstacles in real time and dynamically adjust the robot’s path, ensuring safe navigation even in changing environments. |
| − | | | + | | [https://github.com/ros-navigation/navigation2/tree/main/nav2_collision_monitor Doc] |
|} | |} | ||
</div> | </div> | ||
Latest revision as of 04:16, 10 June 2025
Introduction
Advantech Robotic Suite offers five main categories of ROS2 services specifically designed for AMR applications, helping users accelerate development. These services include a variety of applications, algorithms and reference documentation to support efficient integration and deployment.
Planning
| Application | Description | Reference |
|---|---|---|
| Waypoint Following | Within Nav2, waypoint following is a feature that allows a robot to autonomously navigate through a series of specified points (waypoints) in sequence. It leverages the navigation stack’s planning and control capabilities to ensure accurate and reliable traversal of a given route. | Doc |
| Path Planning | Path planning in Nav2 refers to the process where the navigation stack computes a feasible and efficient path from the robot’s current location to its target destination. This process considers the environment’s obstacles and robot’s kinematics, using built-in planning algorithms. | Doc |
Visual Perception
| Application | Description | Reference |
|---|---|---|
| Barcode Recognition | Basic ROS2 wrapper for the zbar barcode reader library. Reads image stream from image topic, and outputs detected barcodes to barcode topic. Works with 1D and 2D barcodes. | Github |
Sensing Perception
| Application | Description | Reference |
|---|---|---|
| IMU Tools | A set of utilities for processing and visualizing data from Inertial Measurement Units (IMUs), which provide orientation, acceleration, and angular velocity information. | Github |
SLAM
| Application | Description | Reference |
|---|---|---|
| RTAB-Map | Real-Time Appearance-Based Mapping (RTAB-Map) is a graph-based SLAM (Simultaneous Localization and Mapping) algorithm that creates 3D maps using visual, depth, and sensor data. | Github |
| Cartographer | A real-time SLAM algorithm developed by Google that enables robots to build 2D and 3D maps of their environment using laser and odometry data. | Doc Github |
| SLAM Toolbox | A collection of SLAM algorithms and tools for mapping and localization, supporting online and offline map building, pose-graph optimization, and loop closure. | Github |
| LIO-SAM | Lidar-Inertial Odometry via Smoothing and Mapping (LIO-SAM) is a state-of-the-art SLAM system that fuses LiDAR and IMU data to produce accurate, high-frequency odometry and maps. | Github |
| Application | Description | Reference |
|---|---|---|
| Navigation 2 | A ROS2 framework that provides a complete set of navigation features, including localization, path planning, and control, for autonomous robot movement in dynamic environments. | Doc Github |
| Robot Localization | A ROS2 package that fuses data from various sensors (e.g., GPS, IMU, odometry) to provide accurate state estimation of a robot’s position and orientation. | Doc Github |
| Hybrid A* | Hybrid A* is one of the path planning algorithms integrated in Nav2. It enhances traditional A* by considering the robot's motion constraints and continuous state space, making it suitable for robots that require smooth and realistic paths, especially non-holonomic vehicles. | Doc |
| Dijkstra | Dijkstra’s algorithm, available within Nav2’s planning plugins, is used to find the shortest path in a grid map from start to goal. It guarantees the optimal solution and serves as a robust global planner for route generation in the navigation stack. | Doc |
| Obstacle Avoidance | Obstacle avoidance in Nav2 is handled by its local planner and costmap modules. These components work together to detect obstacles in real time and dynamically adjust the robot’s path, ensuring safe navigation even in changing environments. | Doc |