Building Intelligent Robots: From Machine Learning to Autonomous Navigation

This advanced-level course is designed for graduates who are ready to master the complexities of building fully autonomous robotic systems powered by Artificial Intelligence (AI) and Machine Learning (ML). The course goes beyond basic robotics and delves into the world of intelligent systems where perception, decision-making, and control come together to create self-operating robots. Learners will explore how to develop systems capable of navigating dynamic environments, recognizing objects, and making decisions without human intervention.

The course covers cutting-edge concepts such as Simultaneous Localization and Mapping (SLAM), enabling robots to map unknown environments while tracking their position in real-time. Students will dive deep into computer vision technologies, understanding how robots can process visual data to identify and classify objects accurately. The course also introduces advanced motion control, path planning algorithms, and obstacle avoidance techniques to ensure smooth and safe robotic navigation.

Another critical component of the course is IoT (Internet of Things) and cloud integration, where students will learn how to develop smart, connected robots that can operate remotely and process real-time data from sensors and external systems. Through cloud integration, students will enable their autonomous robots to interact with and adapt to their environments, ensuring high-level performance in real-world applications.

Graduates will also gain hands-on experience in AI-powered perception and decision-making by working on deep learning models, neural networks, and machine learning algorithms that enable the robot to make intelligent decisions. The capstone project challenges students to design, build, and deploy a fully autonomous robot that integrates AI, IoT, and advanced motion control technologies to solve real-life problems in industries such as healthcare, manufacturing, or agriculture.

What You Will Learn:

• AI-Powered Robotics and Decision-Making
• Autonomous Navigation and SLAM
• Computer Vision and Object Recognition
• Advanced IoT Integration for Robotics
• Real-World Robotics Applications