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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
- Essential AI components: perception, planning, and control
Development Environment Setup
- 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
- Employing cameras and sensors for environmental perception
- Image classification, object detection, and segmentation using TensorFlow
- Edge detection and contour tracking utilizing OpenCV
- Real-time image streaming and processing workflows
Localization and Sensor Fusion
- Understanding the principles of probabilistic robotics
- Kalman Filters and Extended Kalman Filters (EKF)
- Particle Filters suited for non-linear environments
- Data fusion from LiDAR, GPS, and IMU for accurate localization
Motion Planning and Pathfinding
- Path planning algorithms including Dijkstra, A*, and RRT*
- Obstacle avoidance strategies and environment mapping
- Real-time motion control implementation using PID
- Dynamic path optimization leveraging AI
Reinforcement Learning for Robotics
- Fundamentals of reinforcement learning
- Designing reward-based behaviors for robots
- Q-learning and Deep Q-Networks (DQN)
- Integration of RL agents in ROS for adaptive motion control
Simultaneous Localization and Mapping (SLAM)
- Key concepts and workflows in SLAM
- Implementation of SLAM using 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 enhanced human-robot interaction
- Integration with IoT and cloud-based 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 systems
- Demonstration of real-time decision-making using AI models
Summary and Future Directions
- Review of essential AI robotics techniques
- Emerging trends in autonomous robotics
- Resources for ongoing professional development
Requirements
- Programming proficiency in Python or C++
- Foundational understanding of computer science and engineering principles
- Knowledge of probability concepts, calculus, and linear algebra
Target Audience
- Professional Engineers
- Robotics Enthusiasts
- Researchers specializing in automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.