Day 1:
Foundations of Robotics and AI
Overview of robotics: history, types, and applications.
Introduction to AI: machine learning, neural networks, and deep learning.
Key components of robotic systems: sensors, actuators, and controllers.
Case study: Autonomous drones in agriculture.
Day 2:
Designing Robotic Systems
Principles of mechanical design for robotics.
Programming basics for robotics: Python and ROS (Robot Operating System).
Integration of AI algorithms into robotic workflows.
Hands-on exercise: Building a simple robotic arm.
Day 3:
Ethical and Regulatory Considerations
Ethical challenges in AI and robotics: bias, transparency, and accountability.
Compliance with international standards (e.g., ISO 10218, GDPR).
Risk assessment and mitigation strategies for robotic systems.
Group discussion: Balancing innovation with responsibility.
Day 4:
Advanced Applications and Machine Learning
Collaborative robots (cobots): benefits and use cases.
Reinforcement learning for robotics: theory and practice.
Real-time data processing and decision-making in AI systems.
Workshop: Developing a machine learning model for object recognition.
Day 5:
Strategic Implementation and Future Trends
Frameworks for managing robotics projects: Agile and Design Thinking.
Emerging trends: swarm robotics, quantum computing, and edge AI.
Developing a roadmap for AI and robotics adoption in organizations.
Final project presentation: Applying course concepts to a real-world scenario.
