Robotics Fundamentals with AI Course – 4 Weeks

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.

Aim

This program provides a comprehensive understanding of how AI integrates with robotics to design, build, and program intelligent robots. Participants will learn essential robotics principles combined with AI techniques for autonomous decision-making.

Program Objectives

Master Robotics Fundamentals: Understand the core components of robotics, from kinematics to dynamics.
Integrate AI into Robotics: Learn how to apply AI for robot perception, decision-making, and control.
Develop Motion Planning and Navigation: Implement algorithms for robot path planning and motion control.
Hands-On Robot Building: Gain practical experience designing and programming AI-powered robots.
Explore Reinforcement Learning for Robotics: Apply reinforcement learning techniques to enhance autonomous behavior.

Program Structure

Module 1: Introduction to Robotics and AI

Overview of robotics: history, trends, and applications.
Role of AI in sensing, planning, and controlling robotic systems.
Types of robots: Mobile robots, industrial robots, and humanoids.

Module 2: Robot Kinematics and Dynamics

Forward and inverse kinematics for robotic motion.
Robot arm manipulation and movement strategies.
Dynamics of robotic systems and motion analysis.

Module 3: Perception and Sensors in Robotics

Types of sensors: LIDAR, Cameras, Proximity Sensors, IMUs.
Sensor fusion: Combining data from multiple sources for accurate perception.
AI-based perception techniques: Visual recognition and depth sensing.

Module 4: Path Planning and Motion Control

Classical path planning algorithms: Dijkstra, A*.
AI-based path planning: Probabilistic Roadmap, RRT, and reinforcement learning.
Motion control strategies: PID control, trajectory planning.

Module 5: Computer Vision in Robotics

Basics of computer vision for robotic systems.
AI techniques for object detection and recognition.
Real-time image processing in robotics (e.g., SLAM).

Module 6: Localization and Mapping

Simultaneous Localization and Mapping (SLAM) for robot navigation.
AI techniques for localization (EKF, Particle Filters, Visual SLAM).
Applications in autonomous vehicles and drones.

Module 7: Robotic Manipulation and Grasping

AI techniques for robotic manipulation and grasping.
Robot hands and end effectors for various tasks.
Grasp planning, force sensing, and manipulation strategies.

Module 8: Robotics Operating Systems (ROS)

Introduction to ROS and its architecture for robotics.
Building and simulating robots in ROS environments.
AI integration in ROS-based systems for enhanced functionality.

Module 9: Reinforcement Learning for Robotics

Basics of reinforcement learning: Q-Learning, Policy Gradients.
Applying reinforcement learning to robotic tasks.
Case studies: Robotic arm control, autonomous drones.

Module 10: Human-Robot Interaction (HRI)

Designing robots for human interaction in real-world environments.
AI for speech recognition and natural language processing in robotics.
Ethical considerations and challenges in human-robot collaboration.

Module 11: Autonomous Robots and AI

AI techniques for autonomous navigation in mobile robots.
Sensor fusion and real-time decision-making for robots.
Case studies: Autonomous vehicles, warehouse robots, drones.

Participant Eligibility

Robotics Engineers: Working on mechanical or AI-driven robot systems.
AI Specialists: Focused on integrating AI into autonomous systems.
Data Scientists: Developing AI models for robotics tasks.
Software Developers: Building applications for AI-driven robotic systems.

Program Outcomes

Design and Build Intelligent Robots: Gain the skills to design, build, and program AI-powered robots.
AI for Robot Perception and Control: Master AI techniques for perception, navigation, and control in robots.
Proficiency in Kinematics and Learning Algorithms: Learn kinematics, motion planning, and reinforcement learning for robotics.
Hands-On Experience: Apply AI in real-world robotics projects, enhancing practical knowledge in autonomous systems.

Program Deliverables

Access to e-LMS: Full access to all course materials and online resources.
Real-Time Project: Engage in a hands-on robotics project powered by AI.
Project Guidance: Expert mentorship throughout the development of your robotics project.
Research Publication Opportunity: Support for publishing findings on AI-driven robotics.
Final Examination: Certification awarded based on mid-term assignments and final project submission.
e-Certification: Digital certificate awarded upon successful course completion.

Future Career Prospects

Robotics Engineer: Design, develop, and implement robotic systems for industrial, commercial, or service applications.
AI Specialist in Robotics: Focus on integrating AI techniques like perception, decision-making, and control in robots.
Automation Engineer: Develop automated solutions using robotics and AI for various industries.
AI Researcher in Autonomous Systems: Conduct research in AI-driven robotics for navigation, decision-making, and human-robot interaction.
Industrial Robotics Developer: Design robotic systems for manufacturing and logistics automation.
Embedded Systems Engineer: Build embedded systems for AI-based robotic platforms.

Job Opportunities

Industrial Automation Companies: Focused on automating tasks using robotics and AI.
Autonomous Vehicle Companies: Developing self-driving technologies for cars, drones, or delivery systems.
Robotics Startups: Building intelligent service robots for healthcare, logistics, or hospitality.
Research Institutions: Developing advanced robotics solutions with AI.

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	4 Weeks

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Robotics Fundamentals with AI Course – 4 Weeks

Aim

Program Objectives

Program Structure