ROS2 Migration and Industrial Simulation Pipeline
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Welcome to the Industrial Robotics Accellerator Program
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Required Material and Reccomended Setup
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Repository for this Module
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Migration strategy (9:22)
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Migrate custom robot description package (28:23)
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Robot Control and ROS2 Control (54:23)
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ROS2 Control: setup and deploy Gripper Action Controller (16:20)
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ROS2 Control: setup and deploy Joint Trajectory Controller (12:19)
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ROS2 Control: Control the robot sending trajectory_msgs from C++ script (11:43)
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Replicate the .world from ROS1 (8:31)
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Simulating a RealSense-like Depth Camera in Gazebo and Visualizing the Stream in RViz2 (25:45)
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Create MoveIt2 configuration package for a custom robot (55:40)
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Connecting Gazebo and MoveIt2 -- Controlling the robot from Rviz and seing the motion executed in Gazebo (18:24)
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Create an inverse kinematics node with MoveIt2 in C++ (23:39)
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How to simulate grasping with LinkAttacher in Gazebo (11:38)
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First Pick and Place Application with a Custom 6-Axis Robot in Gazebo and MoveIt2 (30:27)
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UR5, Depth Camera setup in Gazebo with MoveIt (81:24)
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Add Robotiq gripper to UR5 and setup in Gazebo with MoveIt (62:50)
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Setup the world and LinkAttacher for grasping (15:34)
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Test inverse kinematics with UR5 (20:46)
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Run Pick and Place with UR5 and Robotiq gripper (8:26)
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Point Cloud Library and 3D processing in ROS2 (52:12)
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Pick and Place with UR5 Robotiq Gripper using 3D camera in ROS2 (31:58)
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Resources and GitHub repository of the master class
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How to make your packages portable
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Fixing MoveIt Setup Assistant (MSA) Crash after ROS2 Updates
AI Robotics Applications and LLM Integration in ROS2
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Setting Up the ROS2 Workspace for AI Robotics Applications and LLM Integration (12:02)
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Building the Custom Package my_xarm6: Robot, World, and Camera Setup (63:25)
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Creating the my_xarm6_app Package: First Motion Node (26:46)
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CNC Motion Node: Drawing Circles and Squares with Constant Orientation (37:50)
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Blind Pick and Place: Fixed Object Positions (29:03)
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Vision Integration with OpenCV: Depth Reconstruction via Intrinsics (38:32)
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Vision-Based Pick and Place + Custom Service Definition (35:17)
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Introduction to Ollama and LLM Integration (12:17)
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LLM Task Pipeline: Designing the Action-Oriented Architecture and System Prompt engineering (55:45)
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LLM-Driven Pick and Place: Updating System Prompt + Vision + Motion Integration (45:48)
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Test Robotics Tasks with LLM Ollama integrated in ROS2 (21:26)
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General Task LLM: Unified Prompt and Full Robot Commanding (CNC, pick place with vision, end effector control)) (24:59)
Simulation-to-Real Industrial Robotics Deployment with FR3WML + Jetson + Vision AI
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Module available on April 2026
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Simulation to Reality – Building a Real Industrial Robotic Pipeline - Introduction (9:06)
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Reference Repositories for Real Industrial Deployment
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Setup comunication between PC and Robot Hardware controller (8:22)
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Understanding the Robot Driver Layer: How to Interface Any Robot Controller with ROS2 (26:54)
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How to Create a Bridge to Interface MoveIt with Robot Controller (33:50)
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Gripper Hardware setup (6:54)
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Bridging Digital Outputs to ROS2 Gripper Control (20:54)
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Building a Custom ROS2 Robot Package: Adding a Gripper and Camera to the FR3WML (18:17)
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Designing a Real ROS2 Robot Architecture with MoveIt (12:21)
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Integrating a Gripper (SoftGripper) into MoveIt (47:59)
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Control Robot Speed from ROS2 and MoveIt (24:33)
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Move the Real Robot with MoveIt Inverse Kinematics (22:42)
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Hardware Architecture for Pick and Place with 3D Camera (17:31)
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Jetson Orin Nano Setup for Real-Time RealSense Streaming with Docker (31:24)
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Software Architecture -- From Camera Streaming to Local AI Inference (29:47)
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Create the Inference Container on Jetson Orin Nano (YOLO + ROS2) (21:44)
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3D Pose Estimation with YOLO + RealSense on Jetson (17:56)
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Handle Real Images noisy and 3D Pose Publishing (17:29)
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6D Pose Estimation with YOLO + Realsense on Jetson (36:21)
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Industrial Pick and Place Framework: Bottle from Crate and Capsule on Bottle Neck (29:08)
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From Monolithic Code to an Industrial Behavior Tree Framework (28:41)
Wrap up and next steps
Additional module to be defined based on student's needs and requests
