ROB501 FUNDAMENTALS & EMERGING TOPICS IN ROBOTICS

Course Objectives

Objectives: To teach students students 

1.     The necessary linear algebra knowledge and programming skills for robotic applications,

2.     The principles of kinematics and dynamics in rigid bodies,

3.     The basics of state estimation in the Bayesian framework,

4.     The fundamentals of modern control theory,

5.     The basics of motion planning.

Course Content

Transformations,robot operating systems, kinematics, dynamics.State-space techniques: Controllability, observability,pole placement and estimator design. Discrete-time control systems.Bayesian estimation theory: Bayesian recursion, Kalman filters, extended Kalman filter, interacting multiple filters. Configuration space, roadmaps, graph search techniques, planning methods.

Course Learning Outcomes

Students will

1.     Be able to apply the tools of linear algebra to understand the rigid body transformations,

2.     Become familiar with Robot Operating System (ROS),

3.     Be able to conduct a kinematical analysis for the plane motion of rigid bodies,

4.     Identify and formulate problems in rigid body dynamics,

5.     Generate probabilistic state-space models from simple descriptions,

6.     Apply Kalman filter to basic problems,

7.     Be able to use EKF and IMM when necessary,

8.     Understand the behaviour of PID controller,

9.     Generate state-space models for simple systems,

10.  Be able to design controllers and observers to satisfy certain requirements,

11.  Understand the concept of configuration space,

12.  Be able to derive the forward kinematic equations for simple examples,

13.  Apply the road map methods and the graph search algorithms,

14.  Gain familiarity with the the approaches in probabilistic planning and feedback motion planning.