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Course Outline
Introduction to Advanced Robotics and AI Integration
- Contextualizing robotics within Industry 4.0
- The role of AI in sensing, strategy, and command
- Relevant software and simulation platforms
Sensing Systems and Sensor Fusion
- Computer vision applications in robotics (2D/3D cameras, LiDAR)
- Techniques for sensor calibration and data fusion
- Object identification and environmental mapping
Deep Learning for Sensing
- Neural networks applied to visual recognition
- Utilizing TensorFlow or PyTorch with robotic datasets
- Training sensing models for object tracking
Motion Planning and Route Optimization
- Sampling-based and optimization-driven planning methods
- Implementing motion planning with MoveIt
- Collision prevention and dynamic route replanning
Learning-Driven Control Strategies
- Reinforcement learning for robotic command
- Embedding AI into low-level control loops
- Simulation via OpenAI Gym and Gazebo
Collaborative Robots (Cobots) in Intelligent Manufacturing
- Safety regulations and human-robot interaction
- Programming and integrating cobots with AI capabilities
- Adaptive behaviors and real-time responsiveness
System Integration and Deployment
- Interface development with industrial controllers (PLC, SCADA)
- Edge AI deployment for real-time robotic operations
- Data logging, monitoring, and diagnostic troubleshooting
Conclusion and Future Directions
Requirements
- Foundational knowledge of robotic systems and kinematic principles
- Proficiency in Python programming
- Acquaintance with artificial intelligence or machine learning principles
Target Audience
- Robotics Engineers
- Systems Integrators
- Automation Managers
21 Hours
