Smart Robotics in Manufacturing: AI for Perception, Planning, and Control Training Course

Foundations of Smart Robotics and AI Integration

• The landscape of robotics in Industry 4.0
• The critical role of AI in perception, planning, and control
• Essential software tools and simulation environments

Perception Systems and Advanced Sensor Fusion

• Computer vision applications for robotics (including 2D/3D cameras and LiDAR)
• Techniques for sensor calibration and data fusion
• Methods for object detection and environmental mapping

Deep Learning Applications in Perception

• Utilizing neural networks for visual recognition tasks
• Working with TensorFlow or PyTorch using robotic datasets
• Training perception models focused on object tracking

Motion Planning and Path Optimization Strategies

• Exploring sampling-based and optimization-based planning approaches
• Implementing motion planning using MoveIt
• Strategies for collision avoidance and dynamic re-planning

Learning-Based Control Strategies

• Applying reinforcement learning for robotic control
• Embedding AI into low-level control loops
• Simulation practices using OpenAI Gym and Gazebo

Collaborative Robots (Cobots) in Smart Manufacturing

• Reviewing safety standards and human-robot collaboration protocols
• Programming and integrating cobots with AI capabilities
• Implementing adaptive behaviors for real-time responsiveness

System Integration and Deployment

• Interfacing with industrial controllers such as PLC and SCADA
• Deploying Edge AI for real-time robotic applications
• Managing data logging, monitoring, and troubleshooting