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Course Outline
Introduction to AI and Robotics
- The convergence of 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 configuration of Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments via Jupyter Notebooks
Perception and Computer Vision
- Leveraging cameras and sensors for environmental perception
- Image classification, object detection, and segmentation with TensorFlow
- Edge detection and contour tracking using OpenCV
- Real-time image streaming and processing workflows
Localization and Sensor Fusion
- Principles of probabilistic robotics
- Kalman Filters and Extended Kalman Filters (EKF)
- Particle Filters for non-linear environments
- Localization via integration of LiDAR, GPS, and IMU data
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*
- Obstacle avoidance strategies and environment mapping
- Real-time motion control utilizing PID
- Dynamic path optimization driven by AI
Reinforcement Learning for Robotics
- Foundational concepts of reinforcement learning
- Designing reward-based behaviors for robots
- Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents in ROS for adaptive motion control
Simultaneous Localization and Mapping (SLAM)
- Core concepts and workflows of SLAM
- Implementing SLAM with ROS packages (gmapping, hector_slam)
- Visual SLAM 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
- Ethics and safety considerations in AI-enabled robotics
Capstone Project
- Design and simulation of an intelligent mobile robot
- Implementation of navigation, perception, and motion control
- Demonstrating real-time decision-making using AI models
Summary and Next Steps
- Recap of key AI robotics techniques
- Future trends in autonomous robotics
- Resources for continued professional development
Requirements
- Proficiency in programming with Python or C++
- Fundamental knowledge of computer science and engineering principles
- Basic familiarity with probability, calculus, and linear algebra
Target Audience
- Engineers
- Robotics enthusiasts
- Researchers specializing in automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.