Day 1: Introduction to Computer Vision and AI in Robotics

Session 1: Fundamentals of Computer Vision

• What is computer vision? Key concepts and applications
• The role of computer vision in robotics: perception, analysis, and control
• Overview of image processing techniques: edge detection, filtering, and segmentation

Session 2: Hands-on Lab: Introduction to OpenCV for Image Processing

• Setting up Python and OpenCV for computer vision projects
• Reading, displaying, and manipulating images and video streams
• Practical exercise: Applying basic image processing techniques (grayscale conversion, blurring, edge detection)

Session 3: AI and Machine Learning in Computer Vision

• Introduction to AI algorithms for visual recognition
• Overview of supervised learning for image classification
• Case study: AI-powered facial recognition systems in security applications

Session 4: Hands-on Lab: Building a Simple Image Classifier with Python

• Using Scikit-learn to create a basic machine learning model for image recognition
• Training and evaluating the model on image datasets
• Practical exercise: Classifying objects in images using AI

Day 2: Object Detection and Tracking with AI

Session 1: Advanced Object Detection Techniques

• Introduction to object detection algorithms: Haar cascades, YOLO, SSD, and Faster R-CNN
• How AI models detect objects in real-time for robotics applications
• Case study: Object detection in autonomous vehicles

Session 2: Hands-on Lab: Real-Time Object Detection Using OpenCV and YOLO

• Implementing YOLO (You Only Look Once) for real-time object detection
• Setting up object detection pipelines for camera feeds
• Practical exercise: Detecting multiple objects in real-time video streams

Session 3: Object Tracking Algorithms for Robotics

• Introduction to tracking algorithms: Meanshift, Camshift, Kalman filters, and correlation trackers
• Applications in surveillance, robotics navigation, and automation
• Case study: Object tracking in drone-based delivery systems

Session 4: Hands-on Lab: Implementing Object Tracking with OpenCV

• Tracking moving objects using video feeds
• Combining object detection and tracking for dynamic environments
• Practical exercise: Building a real-time object tracking system with live camera input

Day 3: Autonomous Navigation Using Computer Vision

Session 1: Introduction to Autonomous Navigation in Robotics

• Basics of robotic navigation: path planning, obstacle avoidance, and localization
• The role of computer vision in visual SLAM (Simultaneous Localization and Mapping)
• Case study: AI-powered autonomous navigation in self-driving cars

Session 2: Hands-on Lab: Visual-Based Robot Navigation Using ROS and OpenCV

• Setting up the Robot Operating System (ROS) for navigation simulations
• Integrating computer vision algorithms with ROS for autonomous movement
• Practical exercise: Developing a simple visual navigation system for a simulated robot

Session 3: Sensor Fusion for Enhanced Navigation

• Combining data from cameras, LiDAR, and IMUs for robust navigation
• Introduction to Kalman filters and sensor fusion techniques
• Case study: Sensor fusion in autonomous drones and robotics applications

Session 4: Hands-on Lab: Implementing Sensor Fusion for Autonomous Navigation

• Integrating multiple sensors for improved obstacle detection and path planning
• Practical exercise: Navigating a robot in a dynamic environment using sensor fusion

Day 4: Advanced Applications and Capstone Project

Session 1: Deep Learning for Advanced Computer Vision Tasks

• Introduction to Convolutional Neural Networks (CNNs) for image recognition
• Transfer learning and fine-tuning pre-trained models for custom applications
• Case study: Using deep learning for defect detection in manufacturing robotics

Session 2: Hands-on Lab: Building and Deploying a CNN with TensorFlow

• Developing a CNN model for advanced object recognition
• Training and evaluating the model with custom image datasets
• Practical exercise: Deploying the CNN model on a robotics simulation platform

Session 3: Ethical Considerations in AI-Driven Robotics

• Addressing bias, fairness, and transparency in AI models
• Ensuring safety and security in AI-powered robotic systems
• Case study: Ethical challenges in facial recognition and surveillance robots

Session 4: Capstone Project: Designing an AI-Powered Robotic Navigation System

• Team project: Design, implement, and present an AI-driven computer vision system for autonomous navigation
• Applying object detection, tracking, and path planning algorithms
• Group presentations with peer feedback and instructor evaluation

Session 5: Course Wrap-Up and Key Takeaways

• Recap of key concepts: computer vision, AI integration, and robotics applications
• Best practices for deploying AI-powered computer vision systems in real-world robotics projects
• Final Q&A session to address participants’ specific questions
• Resources for continuous learning in AI, computer vision, and robotics