This course is designed to equip students with fundamental theories and computational methodologies used in designing and analyzing autonomous robotic systems. Specific subjects include: representations of 3D rotations and modeling of rigid body motion; forward and inverse kinematics of manipulators; Jacobian relations; singularities; dynamic modeling of manipulators; path planning and object detection algorithms; motion and force tracking controllers; numerical algorithms to simulate robots and hardware-in-the-loop controllers for closed-loop control of robotic systems. A team project will emphasize an integrated analysis and control of a robotic system.

This course is designed to equip students with fundamental theories and computational methodologies used in designing and analyzing autonomous robotic systems. Students will learn how to formulate kinematic and dynamic equations for robot manipulators analytically, how to synthesize trajectory and force tracking controllers, how to design intelligent algorithms, as well as how to utilize numerical algorithms to simulate robots and how to implement real-time hardware-in-the-loop controllers for such closed-loop control of robotic systems.

During the first part of the course, students will be introduced to rigid motions in space and homoge-
neous transformations, forward and inverse kinematics at configuration and velocity levels, and Lagrange’s equations. Computer-aided dynamic simulations with numerical integration methods will be exercised. During the second part of the course, students will be introduced to path/trajectory planning methods and vision-based object detection, as well as fundamental techniques for robot control. In particular, independent joint control, multi-variable control, force and impedance control approaches will be introduced and implemented on hardware.

The emphasis in this course is on an integrated understanding of the kinematic/dynamic modeling and
control concepts for robotic manipulators. Real-time hardware-in-the-loop implementation of the controllers is also emphasized, such that students can experience the control challenges of the real world, such as sensor noise/quantization and unmodeled system dynamics.

This course involves a hands-on laboratory component (ME 403L) and a team project where the students are expected to implement their algorithms on sample robotic platforms equipped with various sensors.

Mark W. Spong, Seth Hutchinson, M. Vidyasagar, Robot Modeling and Control, John Wiley & Sons, Inc., 2006.