Course Outline


Overview of Artificial Intelligence (AI) and Robotics

  • Computer-simulated versus physical
  • Robotics as a branch of AI
  • Applications for AI in robotics

Understanding Localization

  • Locating your robot
  • Using sensors to assess location and environment
  • Probability exercises

Learning About Robot Motion

  • Exact and inexact motions
  • Sense and move functions

Using Probability Tools

  • Bayes’ rule
  • Theorem of total probability

Estimating Vehicle State Using Kalman Filter

  • Gaussian processes
  • Measurement and motion
  • Kalman filtering (code, prediction, design, and matrices)

Tracking Your Robotic Car Using Particle Filter

  • State space dimension and brief modality
  • Robot class, robot world, and robot particles

Exploring Planning and Search Methods

  • A* search algorithm
  • Motion planning
  • Compute cost and optimal path

Programming Your AI Robot

  • First search program and expansion grid table
  • Dynamic programming
  • Computing value and optimal policy

Using PID Control

  • Robot motion and path smoothing
  • Implementing PID controller
  • Parameter optimization

Mapping and Tracking Using SLAM

  • Constraints
  • Landmarks
  • Implementing SLAM


Summary and Conclusion


  • Programming experience
  • Basic understanding of computer science and engineering
  • Familiarity with probability concepts and linear algebra


  • Engineers
  21 Hours


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