Robotics Course Outline
I. Introduction to Robotics
Overview
Definition and scope of robotics
History and evolution of robotics
Applications of robotics in various fields (industry, healthcare, service, exploration)
Fundamentals of Robotics
Components of a robot (sensors, actuators, controllers)
Types of robots (manipulators, mobile robots, humanoid robots, aerial robots)
Basic concepts of robot kinematics and dynamics
II. Robot Kinematics
Forward Kinematics
Joint coordinates and configuration space
Homogeneous transformation matrices
Deriving forward kinematic equations
Inverse Kinematics
The inverse kinematics problem
Analytical and numerical solutions
Applications in robotic arm positioning
Velocity Kinematics
Linear and angular velocity
Jacobian matrix and its applications
Singularities and manipulability
III. Robot Dynamics
Rigid Body Dynamics
Newton-Euler formulation
Lagrangian mechanics
Equations of motion for robotic systems
Dynamic Simulation
Simulating robot motion
Using simulation tools (e.g., MATLAB, Gazebo)
Dynamic control strategies
IV. Robot Control
Control Theory Basics
Open-loop and closed-loop control
Proportional, Integral, Derivative (PID) control
Stability and performance analysis
Advanced Control Techniques
Model predictive control (MPC)
Adaptive control
Nonlinear control methods
Practical Control Systems
Implementing control algorithms
Real-time control and embedded systems
Case studies of control in industrial robots
V. Robot Perception
Sensors and Sensing
Types of sensors (proximity, vision, force, inertial)
Sensor integration and calibration
Signal processing for robotics
Computer Vision
Basics of image processing
Object detection and recognition
3D vision and depth sensing
SLAM (Simultaneous Localization and Mapping)
Overview of SLAM techniques
Laser-based and vision-based SLAM
Applications in autonomous navigation
VI. Robot Motion Planning
Path Planning
Graph-based methods (Dijkstra, A*)
Sampling-based methods (RRT, PRM)
Optimization-based planning
Trajectory Planning
Polynomial and spline trajectories
Time-optimal and energy-optimal trajectories
Real-time trajectory generation
Motion Planning for Mobile Robots
Navigation algorithms
Obstacle avoidance techniques
Multi-robot coordination
VII. Robot Learning and AI
Introduction to Robot Learning
Machine learning basics
Supervised, unsupervised, and reinforcement learning
Applications in robotics
Reinforcement Learning in Robotics
Markov decision processes (MDPs)
Q-learning, Deep Q-Networks (DQNs)
Policy gradient methods
Deep Learning for Robotics
Neural network architectures
Convolutional Neural Networks (CNNs) for vision
Applications of deep learning in perception and control
VIII. Human-Robot Interaction
Human-Robot Collaboration
Safety and ergonomics
Shared control and teleoperation
Admission Open for this course
Contact Number: 03307615544