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Course Outline
Introduction to Intelligent Robotics and AI Integration
- Overview of robotics in the context of Industry 4.0
- The role of AI in perception, planning, and control
- Software and simulation environments
Perception Systems and Sensor Fusion
- Computer vision for robotics (2D/3D cameras, LiDAR)
- Techniques for sensor calibration and fusion
- Object detection and environmental mapping
Deep Learning for Perception
- Neural networks for visual recognition
- Utilizing TensorFlow or PyTorch with robotic datasets
- Training perception models for object tracking
Motion Planning and Path Optimization
- Sampling-based and optimization-based planning methods
- Utilizing 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 using 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 applications
- Data logging, monitoring, and troubleshooting
Summary and Next Steps
Requirements
- Knowledge of robotic systems and kinematics.
- Proficiency in Python programming.
- Familiarity with AI or machine learning principles.
Target Audience
- Robotics engineers.
- Systems integrators.
- Automation leads.
21 Hours
