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Duration 21 hours
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
- Core 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
- Employing Jupyter Notebooks for conducting AI experiments
Perception and Computer Vision
- Leveraging cameras and sensors for perception tasks
- 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
- Comprehending the principles of probabilistic robotics
- Applying Kalman Filters and Extended Kalman Filters (EKF)
- Using Particle Filters for navigating non-linear environments
- Fusing data from LiDAR, GPS, and IMU for precise localization
Motion Planning and Pathfinding
- Exploring path planning algorithms: Dijkstra, A*, and RRT*
- Implementing obstacle avoidance and environment mapping
- Achieving real-time motion control using PID
- Optimizing dynamic paths with AI-driven techniques
Reinforcement Learning for Robotics
- Grasping the 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 control
Simultaneous Localization and Mapping (SLAM)
- Understanding SLAM concepts and associated workflows
- Implementing SLAM using ROS packages (gmapping, hector_slam)
- Utilizing Visual SLAM with OpenVSLAM or ORB-SLAM2
- Testing SLAM algorithms within simulated environments
Advanced Topics and Integration
- Incorporating speech and gesture recognition for human-robot interaction
- Integrating with IoT and cloud robotics platforms
- Applying 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 systems
- Demonstrating real-time decision-making capabilities using AI models
Summary and Next Steps
- Reviewing key AI robotics techniques
- Exploring future trends in autonomous robotics
- Identifying resources for continued learning
Requirements
- Programming proficiency in Python or C++
- 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
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.