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Course Outline
Introduction to AI and Robotics
- An overview of the intersection between modern robotics and AI.
- Applications in autonomous systems, drones, and service robots.
- Essential AI components: 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 within Jupyter Notebooks.
Perception and Computer Vision
- Employing cameras and sensors for perception tasks.
- Performing image classification, object detection, and segmentation with TensorFlow.
- Carrying out edge detection and contour tracking using OpenCV.
- Managing real-time image streaming and processing.
Localization and Sensor Fusion
- Exploring the principles of probabilistic robotics.
- Application of Kalman Filters and Extended Kalman Filters (EKF).
- Utilizing Particle Filters for non-linear environments.
- Combining LiDAR, GPS, and IMU data for precise localization.
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*.
- Techniques for obstacle avoidance and environment mapping.
- Applying AI for dynamic path optimization.
Reinforcement Learning in Robotics
- Designing robotic behaviors based on reward mechanisms.
- Q-learning and Deep Q-Networks (DQN).
- Integrating RL agents into ROS for adaptive motion.
Simultaneous Localization and Mapping (SLAM)
- Comprehending SLAM concepts and operational workflows.
- Implementing SLAM via 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-based 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.
- Demonstrating real-time decision-making using AI models.
Summary and Future Directions
- Recap of key AI robotics techniques.
- Emerging trends in autonomous robotics.
- Resources for ongoing professional development.
Requirements
- Proficiency in Python or C++ programming.
- Foundational knowledge of computer science and engineering principles.
- Working familiarity with probability concepts, 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.