Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- 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 with Jupyter Notebooks
Perception and Computer Vision
- Employing cameras and sensors for perception tasks
- Performing image classification, object detection, and segmentation using TensorFlow
- Edge detection and contour tracking via OpenCV
- Real-time image streaming and processing workflows
Localization and Sensor Fusion
- Understanding the principles of probabilistic robotics
- Kalman Filters and Extended Kalman Filters (EKF)
- Particle Filters suited for non-linear environments
- Fusing LiDAR, GPS, and IMU data for precise localization
Motion Planning and Pathfinding
- Path planning algorithms: Dijkstra, A*, and RRT*
- Obstacle avoidance strategies and environment mapping
- Real-time motion control implementation using PID
- Dynamic path optimization leveraging AI
Reinforcement Learning for Robotics
- Core fundamentals of reinforcement learning
- Designing robotic behaviors based on reward mechanisms
- 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)
- 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 solutions for robots
- Ethical considerations 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 capabilities using AI models
Summary and Future Directions
- Review of essential AI robotics techniques
- Emerging trends in autonomous robotics
- Resources for continued professional development
Requirements
- Proficiency in programming with Python or C++
- Foundational knowledge 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.