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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
- Installation of Python, ROS 2, OpenCV, and TensorFlow
- Utilizing Gazebo or Webots for robot simulation purposes
- Conducting AI experiments using Jupyter Notebooks
Perception and Computer Vision
- Leveraging 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
- Grasping the concepts 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*
- Addressing obstacle avoidance and environment mapping
- Executing real-time motion control using PID
- Optimizing dynamic paths using AI techniques
Reinforcement Learning for Robotics
- Foundational principles of reinforcement learning
- Designing robotic behaviors based on reward systems
- Implementing Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents within ROS for adaptive motion control
Simultaneous Localization and Mapping (SLAM)
- Understanding SLAM concepts and operational workflows
- Implementing SLAM using ROS packages (gmapping, hector_slam)
- Developing Visual SLAM with OpenVSLAM or ORB-SLAM2
- Testing SLAM algorithms in simulated environments
Advanced Topics and Integration
- Incorporating speech and gesture recognition for human-robot interaction
- Connecting with IoT and cloud robotics platforms
- Implementing AI-driven predictive maintenance for robots
- Examining ethics and safety considerations in AI-enabled robotics
Capstone Project
- Designing and simulating an intelligent mobile robot
- Implementing navigation, perception, and motion control systems
- Demonstrating real-time decision-making capabilities using AI models
Summary and Next Steps
- Review of essential AI robotics techniques
- Exploration of future trends in autonomous robotics
- Resources for continued professional development
Requirements
- Programming proficiency in Python or C++
- A fundamental 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.