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
- 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
- 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
- Gaining an understanding of probabilistic robotics
- Applying Kalman Filters and Extended Kalman Filters (EKF)
- Using Particle Filters for non-linear environments
- Integrating LiDAR, GPS, and IMU data for precise localization
Motion Planning and Pathfinding
- Exploring path planning algorithms: Dijkstra, A*, and RRT*
- Implementing obstacle avoidance and environment mapping
- Controlling real-time motion using PID strategies
- Optimizing dynamic paths using AI techniques
Reinforcement Learning for Robotics
- Mastering the fundamentals of reinforcement learning
- Designing reward-based robotic behaviors
- Implementing Q-learning and Deep Q-Networks (DQN)
- Integrating RL agents in ROS for adaptive motion control
Simultaneous Localization and Mapping (SLAM)
- Grasping SLAM concepts and workflows
- Implementing SLAM with 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 features
- 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++
- 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.