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Course Outline
Introduction to Smart Robotics and AI Integration
- Overview of robotics in Industry 4.0
- AI’s role in perception, planning, and control
- Software and simulation environments
Perception Systems and Sensor Fusion
- Computer vision for robotics (2D/3D cameras, LiDAR)
- Sensor calibration and fusion techniques
- Object detection and environment mapping
Deep Learning for Perception
- Neural networks for visual recognition
- Using TensorFlow or PyTorch with robotic data
- Training perception models for object tracking
Motion Planning and Path Optimization
- Sampling-based and optimization-based planning
- Working with MoveIt for motion planning
- Collision avoidance and dynamic re-planning
Learning-Based Control Strategies
- Reinforcement learning for robotic control
- Integrating AI into low-level control loops
- Simulation with OpenAI Gym and Gazebo
Collaborative Robots (Cobots) in Smart Manufacturing
- Safety standards and human-robot collaboration
- Programming and integrating cobots with AI
- Adaptive behaviors and real-time responsiveness
System Integration and Deployment
- Interfacing with industrial controllers (PLC, SCADA)
- Edge AI deployment for real-time robotics
- Data logging, monitoring, and troubleshooting
Summary and Next Steps
Requirements
- An understanding of robotic systems and kinematics
- Experience with Python programming
- Familiarity with AI or machine learning concepts
Audience
- Robotics engineers
- Systems integrators
- Automation leads
21 Hours
Testimonials (1)
a broad approach to the topic, practical knowledge
Jakub Wieczorek - Politechnika Slaska
Course - Introduction to AI in Smart Factories and Industrial Automation
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