CENG0055 Advanced Robot Motion Planning and Control

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG0055
Name Advanced Robot Motion Planning and Control
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Dr. Öğr. Üyesi Barış ATA


Course Goal / Objective

This course aims to introduce students to advanced topics in robot motion planning and control. The course will enable students to develop an in-depth understanding of robot motion planning and control, which is essential for designing complex robotic systems.

Course Content

The course covers advanced algorithms and techniques for robot motion planning, including trajectory generation, optimization-based planning, sampling-based planning, and motion planning under uncertainty. The course also covers advanced topics in robot control, including feedback control, force control, impedance control, and adaptive control.

Course Precondition

Undergraduate-level courses in robotics, mathematics, and computer science

Resources

"Robot Motion Planning and Control" by Steven M. LaValle

Notes

Research papers and articles on advanced topics in robot motion planning and control


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Understand the fundamental principles of robot kinematics and dynamics
LO02 Analyze and design feedback control systems for robots
LO03 Apply force control techniques to manipulate objects
LO04 Apply nonlinear control techniques to manipulate objects
LO05 Apply adaptive control techniques to manipulate objects


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. 4
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 3
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 2
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 2
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. 4
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design.
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning. 2
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 1
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering.
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. 1
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 2
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities. 2


Week Plan

Week Topic Preparation Methods
1 Introduction to Robot Motion Planning and Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
2 Kinematics and Dynamics of Robots Reading the lecture notes Öğretim Yöntemleri:
Anlatım
3 Feedback Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
4 Force Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
5 Trajectory Generation Reading the lecture notes Öğretim Yöntemleri:
Anlatım
6 Optimization-based Planning Reading the lecture notes Öğretim Yöntemleri:
Anlatım
7 Sampling-based Planning Reading the lecture notes Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Reading the lecture notes Ölçme Yöntemleri:
Yazılı Sınav
9 Motion Planning in Complex Environments Reading the lecture notes Öğretim Yöntemleri:
Anlatım
10 Hybrid Planning and Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
11 Nonlinear Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
12 Adaptive Control Reading the lecture notes Öğretim Yöntemleri:
Anlatım
13 Reinforcement Learning for Robotics I Reading the lecture notes Öğretim Yöntemleri:
Anlatım
14 Reinforcement Learning for Robotics II Reading the lecture notes Öğretim Yöntemleri:
Anlatım
15 Review Reading the lecture notes Öğretim Yöntemleri:
Soru-Cevap
16 Term Exams Reading the lecture notes Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Reading the lecture notes Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 14 14
Final Exam 1 28 28
Total Workload (Hour) 154
Total Workload / 25 (h) 6,16
ECTS 6 ECTS

Update Time: 24.05.2024 05:00