ELECTRICAL AND ELECTRONICS ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MCH4454 Humanoid Robotics Fall
Spring
3 0 3 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level: Bachelor
Mode of Delivery: E-Learning
Course Coordinator : Assoc. Prof. MEHMET BERKE GÜR
Course Lecturer(s): Dr. Öğr. Üyesi EMEL DEMİRCAN
Course Objectives: Acquire the fundamentals in robotics and biomechanics for the modeling, simulation, and control of human musculoskeletal systems. Focus given on the definitions, the concepts, and the foundations used in the multi-disciplinary research at the intersection between robotics and biomechanics. Students form reading teams to evaluate classical and recent research articles in robotics and biomechanics and present them to the class. The theory, readings, and student final project presentations aim at engaging students in developing future research questions in robotics.

Learning Outputs

The students who have succeeded in this course;
I. Concisely define and describe the fundamentals of muscle structure, modeling of human musculoskeletal system, production of movement, motion tracking systems, task-space control, and motion reconstruction
II. Define the concepts of Hill-Type muscle model, electromyography, muscle moment arm, joint moment, redundancy, operational space control, task/posture decomposition, and motion reconstruction
III. Critically read, evaluate, and present research articles related to the fundamentals in robotics and biomechanics
IV. Create future research questions and propose applications based on the fundamentals and the current methods of robotics.

Course Content

Fundamentals in humanoid robotics and biomechanics for the modeling, simulation, and control of human musculoskeletal systems. Muscle Structure, Hill-Type Muscle Model, Muscle Parameters, Moment and Moment Arm, Joint Moments, Modeling of Musculoskeletal Geometry; Structure of Human Models: Body, Joint, DOF…; Introduction to Robotics, Spatial Description, Direct/Inverse Kinematics, Jacobian, Manipulator Control; Operational Space Control, Redundancy, Task/Posture Decomposition.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) 50 Year History of Robotics ; Robotics Areas (i.e., haptics, human motion synthesis, biomimetics, humanoid robotics, underwater robotics, teleoperation, surgical robotics, aerial robotics…); Robots and Human; Why to Study Human Movement?
2) Definition of Terms: Muscle Structure; Hill-Type Muscle Model; Muscle Parameters; Moment and Moment Arm; Joint Moments; Modeling of Musculoskeletal Geometry; Structure of Human Models: Body, Joint, DOF… Assumptions and Limitations; Scaling
3) Haptics, Humanoid Platforms; Guest Lecturer
4) Spatial Description, Direct/Inverse Kinematics, Jacobian, Manipulator Control
5) Video (passive optical) capture - Force Plates (GRFs); Calibration & Challenges (Noise/Filtering); EMG Electromyography; New Developments
6) Robotics Foundations; Redundancy; Operational Space Control; Task/Posture Decomposition
7) Whole-Body Control & Simulation; Balance Control; Contact/Constraints; Simulation Frameworks
8) Midterm Exam
9) Human Motion Control; Marker Placement; Motion Control Hierarchy; From Motion Capture to Motion Dynamics
10) Robotics Methods (Belted Ellipsoids); Human Muscular Effort; Acceleration Characteristics; Addition of Constraints (Contact, Physiological Constraints)
11) Applications in Robotics; Applications in Rehabilitation, in Sports Medicine, and in Orthopeadics; Future Perspectives in Robotics and Biomechanics
12) Student Presentations
13) Student Presentations
14) Student Presentations

Sources

Course Notes: Robotics-based Synthesis of Human Motion. PhD tezi, Emel Demircan, Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, Stanford, USA, August 2012.
References: Robotics-based Synthesis of Human Motion, PhD thesis Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, Stanford, USA,August 2012.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 10 % 20
Laboratory 0 % 0
Application 0 % 0
Field Work 0 % 0
Special Course Internship (Work Placement) 0 % 0
Quizzes 0 % 0
Homework Assignments 0 % 0
Presentation 0 % 0
Project 0 % 0
Seminar 0 % 0
Midterms 1 % 20
Preliminary Jury 0 % 0
Final 1 % 40
Paper Submission 1 % 20
Jury 0 % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 17 6 102
Presentations / Seminar 0 0 0
Project 0 0 0
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 0 0 0
Paper Submission 0 0 0
Jury 0 0 0
Final 0 0 0
Total Workload 144

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Adequate knowledge in mathematics, science and electric-electronic engineering subjects; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2) Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4) Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively.
5) Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
6) Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing.
8) Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9) Awareness of professional and ethical responsibility.
10) Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11) Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.