| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| MCH4454 | Humanoid Robotics | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| 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. |
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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. |
| 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. |
| 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 | ||
| 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. |
| 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 | |
| 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 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |