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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
