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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 elements: perception, planning, and control
Configuring the Development Environment
- Installation of Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments with Jupyter Notebooks
Perception and Computer Vision
- Leveraging cameras and sensors for perception tasks
- Performing image classification, object detection, and segmentation with TensorFlow
- Executing edge detection and contour tracking using OpenCV
- Managing real-time image streaming and processing
Localization and Sensor Fusion
- Grasping the principles of probabilistic robotics
- Applying Kalman Filters and Extended Kalman Filters (EKF)
- Using Particle Filters in non-linear environments
- Combining LiDAR, GPS, and IMU data for precise localization
Motion Planning and Pathfinding
- Path planning algorithms including Dijkstra, A*, and RRT*
- Techniques for obstacle avoidance and environment mapping
- Real-time motion control implementation using PID
- Optimizing dynamic paths with AI
Reinforcement Learning in Robotics
- Core concepts of reinforcement learning
- Designing reward-driven robotic behaviors
- Implementing Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents into ROS for adaptive motion
Simultaneous Localization and Mapping (SLAM)
- Understanding SLAM principles and workflows
- Implementing SLAM using ROS packages (gmapping, hector_slam)
- Utilizing Visual SLAM via OpenVSLAM or ORB-SLAM2
- Testing SLAM algorithms within simulated environments
Advanced Topics and Integration
- Speech and gesture recognition for human-robot interaction
- Connecting with IoT and cloud robotics platforms
- AI-driven predictive maintenance for robotic systems
- Ethical and safety considerations 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 through AI models
Summary and Future Directions
- Recap of key AI robotics techniques
- Emerging trends in autonomous robotics
- Resources for ongoing professional development
Requirements
- Coding proficiency in Python or C++
- Foundational knowledge of computer science and engineering principles
- Familiarity with probability theory, calculus, and linear algebra
Target Audience
- Engineers
- Robotics enthusiasts
- Researchers in automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.