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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
- Key AI components: perception, planning, and control
Setting Up the Development Environment
- Installing Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation
- Conducting AI experiments using Jupyter Notebooks
Perception and Computer Vision
- Employing cameras and sensors for 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 for non-linear environments
- Integrating LiDAR, GPS, and IMU data for localization
Motion Planning and Pathfinding
- Exploring path planning algorithms such as Dijkstra, A*, and RRT*
- Implementing obstacle avoidance and environment mapping
- Achieving real-time motion control using PID
- Optimizing dynamic paths using AI
Reinforcement Learning for Robotics
- Foundational concepts of reinforcement learning
- Designing robotic behaviors based on reward mechanisms
- Utilizing Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents in ROS for adaptive motion
Simultaneous Localization and Mapping (SLAM)
- Comprehending SLAM concepts and workflows
- Implementing SLAM using ROS packages (gmapping, hector_slam)
- Performing Visual SLAM with 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
- Implementing AI-driven predictive maintenance for robots
- Addressing ethics 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 Next Steps
- Review of key AI robotics techniques
- Insights into future trends in autonomous robotics
- Resources for continued learning
Requirements
- Programming experience in Python or C++
- A basic understanding of computer science and engineering principles
- Familiarity with probability concepts, calculus, and linear algebra
Audience
- Engineers
- Robotics enthusiasts
- Researchers in automation and AI
21 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.