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AI in Robotics and Autonomous Systems

Artificial Intelligence plays a crucial role in enhancing the capabilities of autonomous systems, which can perceive their environments, make decisions through AI algorithms, and actuate responses. Key components include perception techniques like SLAM, planning methodologies, reinforcement learning, and various applications across domains such as healthcare and autonomous vehicles.

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Sections

  • 1

    What Is An Autonomous System?

    An autonomous system is an intelligent machine capable of perceiving its environment, making decisions, and acting independently.

    Learning Practice

  • 2

    Perception In Robotics

    This section explores how robots perceive their environment using various sensors and computer vision techniques.

    Learning Practice

  • 2.1

    Slam (Simultaneous Localization And Mapping)

    SLAM enables robots to create maps of their surroundings while tracking their own location in real-time.

    Learning Practice

  • 3

    Planning And Navigation

    This section explores how robots use AI techniques to navigate through environments and plan efficient paths.

    Learning Practice

  • 4

    Reinforcement Learning In Robotics

    Reinforcement Learning (RL) teaches robots to learn through trial and error, enabling them to perform tasks like walking, grasping, or balancing effectively.

    Learning Practice

  • 4.1

    Sim-To-Real Transfer

    Sim-to-Real Transfer refers to the process of training robots in simulation environments and applying learned skills to real-world scenarios.

    Learning Practice

  • 5

    Frameworks And Middleware

    This section introduces essential frameworks and middleware that facilitate robotics development, focusing on ROS and simulation tools like Gazebo.

    Learning Practice

  • 5.1

    Ros (Robot Operating System)

    This section introduces ROS (Robot Operating System), a crucial framework for building robot software, providing essential tools and functionalities for message passing and management of robotic systems.

    Learning Practice

  • 5.2

    Gazebo, Webots

    This section discusses Gazebo and Webots, which are essential physics simulators used for testing reinforcement learning and control in robotics.

    Learning Practice

  • 5.3

    Moveit

    MoveIt is a crucial motion planning framework within ROS, facilitating the movement of robotic arms.

    Learning Practice

  • 6

    Applications Of Ai-Powered Robotics

    This section explores various applications of AI-powered robots across different fields such as vehicles, healthcare, and agriculture.

    Learning Practice

References

Chapter 6_ AI in Robotics and Autonomous Systems.pdf

Class Notes

Memorization

What we have learnt

  • AI is integral to robotic p...
  • SLAM and sensor fusion enha...
  • Reinforcement learning allo...

Final Test

Revision Tests

Chapter FAQs