This course will introduce human-robot interaction on the topics of (1) shared autonomy for medical robots, (2) robot operation interface, and (3) robot learning from human teacher. Topic 1 will cover the shared autonomous motion planning for upper limb exoskeleton and mobile humanoid nursing robot; Topic 2 will cover the EMG/IMU interface for wearable robots and the multi-modal interface for robot teleoperation; Topic 3 will cover the modeling and learning of human motor skills and action decision-making intelligence, in order to reproduce and facilitate physical human-robot interaction. The course projects will focus on the application of tele-nursing robots (physical platform) and predictive biomechanical human driver model (simulation). The course work will include but not limited to paper reading and review, implementation of algorithm, human movement data collection and analysis, as well as user study for robot autonomy design evaluation.
This course covers motion planning algorithms and their applications on mobile robots and manipulator robots (arms and hands). Topics include search algorithms, combinatorial and sampled based motion planning methods, manipulation and grasping planning, and path planning with non-holonomic contraints. It also introduces methods for motion planning under uncertainty and via learning from demonstration. Students will work on individual assignments that involve implementation of motion planning algorithm in Matlab and Python, and work team projects on a tele-nursing manipulator mobile robot using python and C/C++.
This course introduces the theory and practice for the motion control of human-compatible robotic systems. Ideally, the motion of a wearable robot system should be dynamically transparent to its operator, sensitively responsible to the voluntary and involuntary motions of its operator. When used for robot-assisted stroke rehabilitation, a wearable robot system is expected to assist to the operator’s motor skills and correct abnormal arm motions resulting from motor disabilities. In this course, students will study the biomechanics of human motion, the theories of human motion control, and the methods for controlling biologically-compatible robots. Students will also experimentally investigate human motions using a Vicon motion capture system, propose and test their hypothesis on human motion control strategies, and implement motion control algorithms on wearable robots and/or arm-like robotic manipulators.
This course introduces students to robotics within manufacturing systems. Topics include: classification of robots, robot kinematics, motion generation and transmission, end effectors, motion accuracy, sensors, robot control, safety systems, and automation. This course is a combination of lecture, laboratory and project work, and utilizes industrial robots. Through the laboratory work, students will become familiar with robotic programming (using the RAPID robotic programming language for the ABB robot) and the robotic teaching mode (the FlexPendant). The experimental component of the laboratory exercise measures the motion and positioning capabilities of robots as a function of several robotic variables and levels, and it includes the use of experimental design techniques and analysis of variance.