Course Overview

.1.Humanoid Robotics -- Definition of walking, dynamic and static gaits -- ZMP (zero moment point) -- Lagrange's
Equations -- Control Algorithms of Humanoid Robotics -- Sensors and Sensing Aspects of Humanoid Robotics
2.Mutisensor Fusion and Integration --Sigal Level Fusion --Pixel Level Fusion --Feature Level Fusion --Decision Level
Fusion --Kalman Filter --Extended Kalman Filter --Particle Filter --Covariance Intersection --Covariance Union --
Dempster-Shafer Evidence Theory 3.Sensing and Control for Robot Motion 4.Interactive Service Robotics
5.Advanced Topics on Robot Sensing and Control 6.Practical examples of robot sensing and control through photos and video demonstrations.

Learning Achievement

The objectives of this course are to let students who have had the basic background of the robot sensing and control issues, approaches with more in depth understanding of theories and practical applications in robot sensing
and control. The idea of this course is to convey the concept that usually sensing and control should not be
separated and they are interdependent in dealing with an intelligent systems, such as an intelligent robotics system.
Firstly, student will learn more advanced robot sensing and control issues in humanoid robot including definition of
walking, dynamics static gaiting issues, control algorithms and the need for robot sensors interact with different
control aspects. The second focus will be the study of advanced issues of sensor fusion and integration.
Synergistic use of multiple sensors by machines and systems enables greater intelligence to be incorporated into
their overall operation. Motivation for using multiple sensors can be considered as response to simple question:
if a single sensor can increase the capability of a system, would the use of more sensors increase it even further ?
In this course, theories of multisensory fusion and its applications to sensory controlled robotics systems which
involves mathematical and statistical issues including combining sensor uncertainty methods for sensor fusion
includes estimation methods, such as covariance Intersection (CI), Kalman Filtering; Classification methods,
such asSupport Vector Machine (SVM) etc. will be presented and discussed. The third focus will be the advanced
robot motionplanning and control issues. The fourth focus will be the advanced topics in interactive service robotics
by using various sensing and control algorithms.Finally, a variety of practical examples of robot sensing and control
will be presented through photos and video demonstrations. After taking this course, it is expected that students
will l get the state of the art knowledge about the advanced core robotics technologies especially in robot sensing
and control.

Competence

Course prerequisites

This course is suitable for senior and graduate students. There is a take home project in addition to the weekly class meets. The final grade will be computed on the basis of the following weights: Take Home Project Report. 25% Project Presentation during the Class. 25% Midterm Exam. 25% Final Exam. 25% TOTAL 100%

Grading Philosophy

Course schedule

Course type

Online Course Requirement

Instructor

Ren C. Luo

Other information

(College of Electrical Engineering and Computer Science) Graduate Institute of Electrical Engineering

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