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AI in Autonomous Vehicles Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
The program delves into the AI and robotics techniques that empower autonomous vehicles, including computer vision, object detection, sensor fusion from LIDAR and radar, and motion planning algorithms. This hands-on course also covers real-world challenges like obstacle avoidance, lane detection, and decision-making for AVs.
Aim
This 4-week course introduces participants to the fundamentals of robotics with an emphasis on AI and its integration into robotic systems. The course covers key robotics concepts, from basic kinematics and control systems to advanced AI techniques used in autonomous robots. Participants will learn how to build and program simple robots, integrate sensors, and use AI algorithms for real-time decision making in robotic applications.
Program Objectives
- Understand the fundamental principles of robotics including motion, control, and navigation.
- Learn how AI algorithms like computer vision, machine learning, and reinforcement learning are applied in robotics.
- Gain hands-on experience in programming robotic systems and integrating them with AI for autonomous decision-making.
- Build a foundational knowledge of sensors and actuators used in robotic systems.
- Develop skills in working with robotic simulation software and programming environments like ROS (Robot Operating System).
Program Structure
Week 1: Introduction to Robotics and Kinematics
- Overview of robotics: history, types, and real-world applications.
- Basic kinematics: motion, coordinate systems, and robot arm movements.
- Introduction to robot design: sensors, actuators, and control systems.
- Hands-on project: Build a basic robotic arm model and simulate simple movements.
Week 2: Robot Control Systems and Motion Planning
- Understanding control systems in robotics: PID controllers, feedback loops, and motion planning algorithms.
- How to control robotic movements with precision and accuracy.
- Explore motion planning algorithms for pathfinding and navigation.
- Hands-on project: Program a simple robot to follow a pre-defined path using a PID controller.
Week 3: AI in Robotics: Machine Learning and Computer Vision
- Introduction to AI in robotics: applying machine learning to improve robot behavior.
- Understanding computer vision: how robots perceive and interpret their environment.
- Using AI for object detection, recognition, and tracking in robotic systems.
- Hands-on project: Implement a simple computer vision algorithm to detect objects using a camera sensor.
Week 4: Autonomous Robots and Reinforcement Learning
- Introduction to autonomous robots: self-navigation, decision-making, and task execution.
- Exploring reinforcement learning for autonomous decision-making and robot training.
- Designing and simulating intelligent robots that learn from their environment.
- Hands-on project: Train a robot to navigate an environment using reinforcement learning algorithms.
Final Project
- Design and implement a robot capable of performing a specific task autonomously (e.g., obstacle avoidance, object sorting, or delivery).
- Integrate sensors and AI algorithms for real-time decision-making and autonomous control.
- Present the final project, including code implementation and simulation results.
Participant Eligibility
- Students and professionals with a background in computer science, robotics, engineering, or AI.
- Anyone interested in learning the principles of robotics and applying AI to robotic systems.
- No prior experience in robotics or AI is required, though familiarity with programming concepts is helpful.
Program Outcomes
- Solid understanding of the fundamental principles of robotics, including kinematics, motion control, and sensors.
- Hands-on experience programming robotic systems with AI algorithms such as computer vision and reinforcement learning.
- Practical knowledge of designing and building robots for real-time decision-making and autonomous tasks.
- Ability to create intelligent robots capable of performing basic tasks autonomously.
Program Deliverables
- Access to e-LMS: Full access to course materials, resources, and videos.
- Hands-on Projects: Build and program robotic systems using real-world sensors and actuators.
- Final Project: Present a final project demonstrating autonomous behavior and AI integration in robotics.
- Certification: Certification awarded upon completion of the course and successful final project submission.
- e-Certification and e-Marksheet: Digital credentials awarded upon course completion.
Future Career Prospects
- Robotics Engineer
- AI Engineer for Robotics
- Embedded Systems Engineer
- Automation Engineer
- Machine Learning Engineer for Robotics
Job Opportunities
- Robotics Companies: Designing, building, and deploying robots for various applications (e.g., healthcare, automotive, logistics).
- Tech Startups: Developing AI-driven robotic solutions for industries like smart manufacturing, agriculture, and consumer electronics.
- Automation Firms: Working on automation solutions for factories, warehouses, and assembly lines.
- Research Institutions: Developing new technologies in robotics and AI for future applications in autonomous systems.
| Category | E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures |
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