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Course Outline
Foundations of Smart Robotics and AI Integration
- Overview of robotics within Industry 4.0
- The role of AI in perception, planning, and control
- Relevant software and simulation environments
Perception Systems and Sensor Fusion
- Computer vision for robotics (including 2D/3D cameras, LiDAR)
- Sensor calibration and fusion methods
- Object detection and environmental mapping
Deep Learning Applications in Perception
- Neural networks for visual recognition tasks
- Utilizing TensorFlow or PyTorch for robotic data
- Training perception models for object tracking
Motion Planning and Path Optimization
- Sampling-based and optimization-based planning approaches
- Using MoveIt for motion planning tasks
- Collision avoidance and dynamic re-planning strategies
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 protocols for human-robot collaboration
- Programming and integrating cobots with AI capabilities
- Adaptive behaviors and real-time responsiveness
System Integration and Deployment
- Interfacing with industrial controllers (PLC, SCADA)
- Edge AI deployment for real-time robotics operations
- Data logging, monitoring, and troubleshooting procedures
Summary and Future Directions
Requirements
- Solid understanding of robotic systems and kinematics
- Proficiency in Python programming
- Familiarity with core AI or machine learning concepts
Target Audience
- Robotics engineers
- Systems integrators
- Automation leads
21 Hours
