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Duration 21 hours
Course Outline
Introduction to Smart Robotics and AI Integration
- The landscape of robotics in Industry 4.0
- The function of AI in perception, planning, and control
- Relevant software and simulation environments
Perception Systems and Sensor Fusion
- Applying computer vision to robotics (2D\/3D cameras, LiDAR)
- Techniques for sensor calibration and fusion
- Methods for object detection and environmental mapping
Deep Learning for Perception
- Utilizing neural networks for visual recognition
- Leveraging TensorFlow or PyTorch with robotic datasets
- Training perception models for object tracking
Motion Planning and Path Optimization
- Sampling-based and optimization-based planning approaches
- Utilizing MoveIt for motion planning
- Collision avoidance and dynamic re-planning
Learning-Based Control Strategies
- Applying reinforcement learning to robotic control
- Embedding 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
- Interface management with industrial controllers (PLC, SCADA)
- Edge AI deployment for real-time robotics
- Data logging, monitoring, and troubleshooting
Requirements
- A solid grasp of robotic systems and kinematics
- Proficiency in Python programming
- Working knowledge of AI or machine learning concepts
Target Audience
- Robotics engineers
- Systems integrators
- Automation leads
Testimonials (1)
How to use and explore AI for daily works