Robotics Fundamentals with AI

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Mentor Based

Program Syllabus

Module 1

About This Course

This program focuses on the integration of AI with robotics, highlighting both hardware and software aspects. Participants will learn about robot kinematics, sensors, actuators, and AI algorithms that enable autonomous navigation, object detection, and manipulation. The course provides hands-on experience in programming robots using AI-driven techniques.

Module 2

Aim

To provide an in-depth understanding of robotics integrated with AI technologies, enabling participants to design, build, and program intelligent robots. The program covers the essential principles of robotics, combined with AI techniques for autonomous decision-making.

Module 3

Program Objectives

Understand the fundamentals of robotics and their components.
Learn to integrate AI into robotic systems for perception and decision-making.
Develop and implement motion planning and navigation algorithms.
Gain hands-on experience in building and programming AI-powered robots.
Explore the applications of reinforcement learning in autonomous robotics.

Module 4

Program Structure

Introduction to Robotics and AI
- Overview of Robotics: History, Trends, and Applications
- Introduction to AI in Robotics: Role of AI in Sensing, Planning, and Control
- Key Areas of Robotics (Mobile Robots, Industrial Robots, Humanoids)
Robot Kinematics and Dynamics
- Forward and Inverse Kinematics
- Robot Manipulation and Arm Movements
- Dynamics of Robotic Systems
Perception and Sensors in Robotics
- Types of Sensors in Robotics (LIDAR, Cameras, Proximity Sensors, IMUs)
- Sensor Fusion: Combining Multiple Sources of Data
- AI for Robotic Perception: Visual Recognition, Depth Sensing
Path Planning and Motion Control
- Basic Path Planning Algorithms (Dijkstra, A*)
- AI-Based Path Planning (Probabilistic Roadmap, RRT, Reinforcement Learning)
- Motion Control Techniques: PID Control, Trajectory Planning
Computer Vision in Robotics
- Basics of Computer Vision for Robots
- AI for Object Detection and Recognition
- Real-Time Image Processing for Robotic Applications (e.g., SLAM)
Localization and Mapping
- Simultaneous Localization and Mapping (SLAM)
- AI Techniques for SLAM (EKF, Particle Filters, Visual SLAM)
- Applications of SLAM in Autonomous Vehicles and Drones
Robotic Manipulation and Grasping
- AI for Robotic Grasping and Manipulation
- Robot Hands and End Effectors
- Grasp Planning and Force Sensing
Robotics Operating Systems (ROS)
- Introduction to ROS and Its Architecture
- Building and Simulating Robots in ROS
- AI Integration in ROS-Based Systems
Reinforcement Learning for Robotics
- Basics of Reinforcement Learning (Q-Learning, Policy Gradients)
- Applying Reinforcement Learning to Robotics Tasks
- Case Studies: Robotic Arm Control, Autonomous Drone Navigation
Human-Robot Interaction (HRI)
- Designing Robots for Human Interaction
- AI for Speech Recognition and Natural Language Understanding in Robots
- Ethical Considerations in Human-Robot Collaboration
Autonomous Robots and AI
- AI for Autonomous Navigation in Mobile Robots
- Sensor Fusion and AI for Real-Time Decision Making
- Case Studies: Autonomous Vehicles, Drones, Warehouse Robots

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.