Robotics

1. Introduction to Robotics

• Fundamentals:

  • History, types (industrial, mobile, humanoid), and applications.

  • Key components: actuators, sensors, controllers, end-effectors.

• Robotics Math:

  • Coordinate frames (Cartesian, polar).

  • Basic linear algebra & trigonometry for kinematics.

• Ethics & Safety: Human-robot interaction, industrial safety standards.

Tools: Simulators (Webots, Gazebo), Python.

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2. Robot Kinematics & Dynamics

• Forward Kinematics: Calculating end-effector position from joint angles.

• Inverse Kinematics: Solving joint angles for desired positions.

• Dynamics:

  • Newton-Euler and Lagrangian formulations.

  • Torque, inertia, and motion equations.

• Trajectory Planning: Cubic splines, minimum-jerk trajectories.

Tools: MATLAB, ROS (Robot Operating System), PyBullet.

 

3. Robot Perception & Sensors

• Sensor Types:

  • LiDAR, cameras, IMUs, ultrasonic, force/torque sensors.

• Computer Vision:

  • OpenCV for object detection/recognition.

  • Depth sensing (stereo vision, RGB-D).

• Simultaneous Localization and Mapping (SLAM):

  • Kalman filters, particle filters, ORB-SLAM.

Tools: ROS, OpenCV, PCL (Point Cloud Library).

 

4. Robot Control Systems

• Control Theory:

  • PID control, state-space control.

  • Feedforward vs. feedback control.

• Motion Control:

  • Position, velocity, and torque control.

  • Impedance control for human-robot interaction.

• AI in Control:

  • Reinforcement learning (e.g., DQN for robotic tasks).

Tools: Arduino, Raspberry Pi, ROS Control.

 

5. Robot Programming & AI Integration

• ROS Basics:

  • Nodes, topics, services, TF (Transform Library).

• Path Planning:

  • A*, Dijkstra, RRT (Rapidly Exploring Random Trees).

• Machine Learning:

  • CNN for vision-based navigation.

  • Reinforcement learning (e.g., robotic arm manipulation).

• Human-Robot Interaction (HRI): Voice commands, gesture recognition.

Tools: ROS, PyTorch, TensorFlow.

 

6. Advanced Topics (Electives)

• Swarm Robotics: Decentralized control, flocking algorithms.

• Soft Robotics: Compliant mechanisms, bio-inspired design.

• Edge AI for Robotics: Deploying ML models on embedded systems (Jetson Nano).