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Course Outline
Introduction to AI and Robotics
- Overview of the convergence between modern robotics and AI.
- Applications in autonomous systems, drones, and service robots.
- Core 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 using Jupyter Notebooks.
Perception and Computer Vision
- Utilizing cameras and sensors for environmental perception.
- 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
- Understanding the principles of probabilistic robotics.
- Implementing Kalman Filters and Extended Kalman Filters (EKF).
- Applying Particle Filters in non-linear environments.
- Fusing LiDAR, GPS, and IMU data for precise localization.
Motion Planning and Pathfinding
- Exploring path planning algorithms: Dijkstra, A*, and RRT*.
- Managing obstacle avoidance and environment mapping.
- Achieving real-time motion control via PID.
- Dynamic path optimization driven by AI.
Reinforcement Learning for Robotics
- Fundamentals of reinforcement learning.
- Designing robotic behaviors based on reward mechanisms.
- Implementing Q-learning and Deep Q-Networks (DQN).
- Integrating RL agents within ROS for adaptive motion.
Simultaneous Localization and Mapping (SLAM)
- Grasping SLAM concepts and workflows.
- Implementing SLAM using ROS packages (gmapping, hector_slam).
- Utilizing Visual SLAM with OpenVSLAM or ORB-SLAM2.
- Testing SLAM algorithms in simulated settings.
Advanced Topics and System Integration
- Enabling human-robot interaction through speech and gesture recognition.
- Integration with IoT and cloud robotics platforms.
- Implementing AI-driven predictive maintenance for robots.
- Discussing ethics 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 using AI models.
Conclusion and Future Directions
- Recap of essential AI robotics techniques.
- Insights into future trends in autonomous robotics.
- Resources for ongoing professional development.
Requirements
- Proficiency in Python or C++ programming.
- A foundational understanding of computer science and engineering principles.
- 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.