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Advanced Robotics Systems Certification (Course 4 of 4)
This Advanced Robotics Systems Certification Course provides an in-depth exploration of cutting-edge robotics technologies, focusing on machine learning, robotics architecture, ...
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Instructor
Jason James
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Advanced Robotics Systems Certification Course
Week 1: Machine Learning for Robotics
Written Lectures:
- Supervised vs. unsupervised learning in robotics (object recognition, path prediction).
- Neural network architectures for LiDAR/camera data processing.
- Model quantization for edge deployment on microcontrollers.
Labs (VS Code + PlatformIO):
- Train a CNN in Python to classify objects from a simulated camera feed.
- Deploy the model on an ESP32 using PlatformIO’s ML quantization tools.
- Compare inference speeds across hardware (ESP32 vs. Raspberry Pi).
Audio Lectures:
- "AI in Autonomous Vehicles" (25 mins).
- "Ethical AI in Robotics" (20 mins).
Assessments:
- Matching Quiz: ML algorithms vs. robotic applications (10 questions).
- MCQ Quiz: 40 questions on neural networks, data preprocessing, and ethics.
Week 2: ROS 2 Architecture & Gazebo Simulations
Written Lectures:
- ROS 2 nodes, topics, and services with DDS middleware.
- Simulating industrial robots (UR5, TurtleBot3) in Gazebo.
- ROS 2 control frameworks for hardware abstraction.
Labs (VS Code + PlatformIO):
- Program a UR5 robot arm to perform pick-and-place tasks.
- Debug ROS 2 networking issues using VS Code’s ROS 2 extension.
- Visualize sensor data with RViz2.
Audio Lectures:
- "ROS 2 in Industrial Automation" (30 mins).
- "Case Study: Amazon Warehouse Robots" (25 mins).
Assessments:
- Matching Quiz: ROS 2 components vs. functions (10 questions).
- MCQ Quiz: 40 questions on Gazebo simulations and DDS middleware.
Week 3: Sensor Fusion & SLAM
Written Lectures:
- Kalman/particle filters for LiDAR-camera fusion.
- SLAM with ROS 2 Nav2 stack and RTAB-Map.
- Real-time mapping in GPS-denied environments.
Labs (VS Code + PlatformIO):
- Fuse IMU and LiDAR data for mobile robot localization.
- Implement SLAM on a TurtleBot3 in a simulated warehouse.
- Optimize mapping accuracy using PlatformIO’s profiling tools.
Audio Lectures:
- "SLAM in Drones and Autonomous Cars" (25 mins).
- "Sensor Fusion Challenges" (20 mins).
Assessments:
- Matching Quiz: SLAM algorithms vs. use cases (10 questions).
- MCQ Quiz: 40 questions on Kalman filters and Nav2.
Week 4: Human-Robot Interaction (HRI)
Written Lectures:
- ISO 10218/15066 safety standards for collaborative robots.
- Emotion recognition with OpenCV and speech synthesis.
- Designing voice-controlled UIs for cobots.
Labs (VS Code + PlatformIO):
- Program a UR3e cobot to respond to voice commands via ROS 2.
- Simulate safety-rated monitored stops using VS Code’s breakpoint debugging.
Machine Learning for Robotics
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1
Supervised vs. Unsupervised Learning in RoboticsIn this engaging lesson, "Robotics Unplugged: Navigating Supervised vs. Unsupervised Learning," students will explore the fundamental concepts of supervised and unsupervised learning within the field of robotics. By the end of the session, learners will be equipped with the ability to distinguish between these two learning paradigms, understand their applications in robotic systems, and appreciate how they impact decision-making processes in autonomous machines. Prepare to dive into the exciting world of robotics and machine learning! -
2
Neural Network Architectures for LiDAR/Camera Data ProcessingIn this lesson, students will delve into advanced neural network architectures specifically designed for processing LiDAR and camera data, enabling them to enhance their skills in multimodal data analysis. Participants will explore cutting-edge techniques and methodologies, learning how to effectively integrate these architectures to improve accuracy and efficiency in real-world applications. By the end of the lesson, students will be equipped with practical knowledge to tackle complex data processing challenges in autonomous systems and beyond.
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3
Model Quantization for Edge Deployment on MicrocontrollersIn this lesson, "Optimizing AI for the Edge: Model Quantization Techniques for Microcontrollers," students will explore the essential techniques of model quantization, enabling them to deploy efficient AI solutions on microcontroller platforms. By the end of the session, participants will gain hands-on experience in reducing model size and improving inference speed without compromising performance, empowering them to create more robust and scalable edge AI applications.
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4
Building a CNN: Classifying Objects from Simulated Camera Feeds in Python -
5
Matching Quiz: ML Algorithms vs. Robotic Applications -
6
MCQ Quiz: Neural Networks, Data Preprocessing, and Ethics
ROS 2 Architecture & Gazebo Simulations
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7
ROS 2 Nodes, Topics, and Services with DDS MiddlewareIn this engaging lesson on "Understanding ROS 2: Mastering Nodes, Topics, and Services with DDS Middleware," students will explore the foundational concepts of ROS 2, including the architecture of nodes, the communication patterns of topics, and the role of services. By delving into the Data Distribution Service (DDS) middleware, learners will gain hands-on experience in building robust robotic applications that effectively manage data flow and inter-process communication.
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8
Simulating Industrial Robots in GazeboIn this lesson, "Mastering Industrial Robot Simulation in Gazebo," students will explore the intricacies of simulating industrial robots using the Gazebo platform. They will gain hands-on experience in setting up robotic models, creating realistic environments, and executing complex tasks, equipping them with essential skills to enhance automation and robotics in real-world applications. By the end of the lesson, learners will confidently navigate Gazebo to effectively simulate and troubleshoot industrial robot operations.
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9
ROS 2 Control Frameworks for Hardware AbstractionIn this lesson, "Mastering ROS 2 Control Frameworks: A Guide to Hardware Abstraction," students will dive into the essentials of the ROS 2 control framework, focusing on how to effectively abstract hardware components for robotic applications. Participants will learn to design and implement robust control systems, enabling seamless interactions between software and hardware, while gaining hands-on experience with key tools and techniques that enhance robotic functionality and performance.
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10
Mastering Automation: Programming the UR5 Robot Arm for Pick-and-Place Tasks
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11
Matching Quiz: ROS 2 Components vs. Functions
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12
MCQ Quiz: Gazebo Simulations and DDS Middleware
Sensor Fusion & SLAM
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13
Kalman/Particle Filters for LiDAR-Camera FusionIn this engaging lesson, students will explore the fundamentals of LiDAR-camera fusion, focusing on the powerful techniques of Kalman and Particle Filters. Through hands-on examples and practical applications, learners will gain a solid understanding of how to enhance data accuracy and improve environmental perception in autonomous systems. By the end of the lesson, students will be equipped with the skills to implement these filtering techniques in real-world scenarios.
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14
SLAM with ROS 2 Nav2 Stack and RTAB-MapIn this lesson, students will dive into the fundamentals of Simultaneous Localization and Mapping (SLAM) by integrating the ROS 2 Nav2 stack with RTAB-Map. Participants will gain hands-on experience in setting up and configuring these powerful tools, enabling them to create advanced robotic navigation and mapping solutions that enhance their understanding of spatial awareness and real-time data processing. Join us to unlock the potential of autonomous robotics through practical applications and innovative techniques!
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15
Real-time Mapping in GPS-Denied EnvironmentsIn this lesson, "Navigating the Unknown: Real-time Mapping Techniques in GPS-Denied Environments," students will explore innovative mapping strategies and technologies used in situations where traditional GPS signals are unreliable or unavailable. Through hands-on activities and case studies, participants will learn how to leverage alternative navigation methods, including sensor fusion and SLAM (Simultaneous Localization and Mapping), to create accurate and reliable real-time maps, enhancing their problem-solving skills in challenging environments.
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16
Integrating IMU and LiDAR Data for Enhanced Mobile Robot Localization
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17
Matching Quiz: SLAM Algorithms vs. Use Cases
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18
MCQ Quiz: Kalman Filters and Nav2
Human-Robot Interaction (HRI)
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19
ISO 10218/15066 Safety Standards for Collaborative RobotsIn this lesson, students will explore the critical ISO 10218 and ISO 15066 safety standards that govern the design and operation of collaborative robots. Through engaging discussions and practical examples, learners will understand how these standards help ensure safe human-robot interaction, equipping them with the knowledge to implement best practices for workplace safety in robotic environments.
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20
Emotion Recognition with OpenCV and Speech SynthesisIn this dynamic lesson, "Unlocking Emotions: Using OpenCV for Emotion Recognition and Speech Synthesis," students will explore the fascinating intersection of computer vision and artificial intelligence. They will learn how to utilize OpenCV for real-time emotion recognition from facial expressions and integrate speech synthesis technology to create responsive, emotionally aware applications. By the end of this lesson, students will not only grasp the technical skills required for emotion detection but also appreciate the broader implications of emotional intelligence in technology.
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21
Designing Voice-Controlled UIs for CobotsIn this lesson, students will explore the principles of designing intuitive voice-controlled interfaces specifically for collaborative robots (cobots). By engaging in hands-on activities, learners will understand how to create user-friendly voice commands that enhance human-robot interaction, ensuring seamless collaboration in various environments. Ultimately, students will gain the skills to innovate and improve the functionality of cobots through effective voice UI design.
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22
Voice-Activated Interaction: Programming a UR3e Cobot with ROS 2
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23
Matching Quiz: HRI Concepts vs. Applications
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24
MCQ Quiz: Safety Standards and Emotion Recognition
What is the difference between supervised and unsupervised learning in robotics?
Supervised learning uses labeled data for training models, enabling tasks like object recognition. Unsupervised learning finds patterns in unlabeled data, useful for tasks like clustering and anomaly detection in robotics.
What tools are used for deploying models on microcontrollers like ESP32?
PlatformIO provides ML quantization tools for optimizing and deploying machine learning models on microcontrollers such as ESP32, allowing efficient execution with limited resources.
How does ROS 2 improve robot programming and simulation?
ROS 2 utilizes nodes, topics, and services with DDS middleware to enhance modularity and communication, making it easier to program and simulate complex robotic systems like UR5 and TurtleBot3.
What are the key safety standards for human-robot interaction?
ISO 10218 and ISO 15066 outline safety requirements for collaborative robots, focusing on minimizing risks during interactions, ensuring safe operation, and establishing guidelines for robot design.
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