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Course Outline
Introduction to AI and Robotics
- An overview of the convergence between modern robotics and AI
- Applications in autonomous systems, drones, and service robots
- Core AI components: perception, planning, and control
Establishing the Development Environment
- Installation and setup of Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments using Jupyter Notebooks
Perception and Computer Vision
- Leveraging cameras and sensors for environmental perception
- Implementing image classification, object detection, and segmentation with TensorFlow
- Performing edge detection and contour tracking using OpenCV
- Managing real-time image streaming and processing
Localization and Sensor Fusion
- Understanding the principles of probabilistic robotics
- Applying Kalman Filters and Extended Kalman Filters (EKF)
- Using Particle Filters for non-linear environments
- Integrating data from LiDAR, GPS, and IMU for precise localization
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*
- Strategies for obstacle avoidance and environment mapping
- Real-time motion control implemented via PID
- Dynamic path optimization driven by AI
Reinforcement Learning for Robotics
- Fundamental concepts of reinforcement learning
- Designing robotic behaviors based on reward mechanisms
- Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents into ROS for adaptive motion control
Simultaneous Localization and Mapping (SLAM)
- Core SLAM concepts and operational workflows
- Implementing SLAM using ROS packages (gmapping, hector_slam)
- Visual SLAM applications using OpenVSLAM or ORB-SLAM2
- Testing SLAM algorithms within simulated environments
Advanced Topics and Integration
- Speech and gesture recognition for human-robot interaction
- Integration with IoT and cloud robotics platforms
- AI-driven predictive maintenance for robotic systems
- Ethical considerations and safety in AI-enabled robotics
Capstone Project
- Designing and simulating an intelligent mobile robot
- Implementing navigation, perception, and motion control modules
- Demonstrating real-time decision-making capabilities using AI models
Summary and Next Steps
- Recap of key AI robotics techniques
- Emerging trends in autonomous robotics
- Resources for continued professional development
Requirements
- Proficiency in programming with Python or C++
- Foundational knowledge of computer science and engineering principles
- Familiarity with probability theory, calculus, and linear algebra
Target Audience
- Professional Engineers
- Enthusiasts and practitioners in the field of Robotics
- Researchers focused on automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.