ECTS - Path Planning and Navigation
Path Planning and Navigation (MECE447) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Path Planning and Navigation | MECE447 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
|
| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course; |
| Course Content | Introduction, kinematic models for mobile robots, mobile robot control, robot attitude, robot navigation, path finding, obstacle mapping and its application to robot navigation, application of Kalman filtering. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction, Locomotion, Direct and Inverse Robot Kinematics, Homogenous Transformations | |
| 2 | Kinematics of Unicycle, Bicycle, Differential Drive, Tricycle, Ackermann and Omnidirectional robots | |
| 3 | Dynamic model for wheeled mobile Robot | |
| 4 | Heading and speed control of front wheel steered vehicle, Heading and speed control of differential drive robot, Computed control for heading and velocity, Pursuit controller, Stanley controller | |
| 5 | Heading and speed control of front wheel steered vehicle, Heading and speed control of differential drive robot, Computed control for heading and velocity, Pursuit controller, Stanley controller | |
| 6 | Taxonomy of driving, perception, sensors, software architecture, environment representation, Rotation matrix for Yaw, Pitch and Roll, Homogenous Transformation matrix, Rotating a vector | |
| 7 | Coordinate systems, Earth-centered Earth-fixed coordinate system, Computing location using Global Positioning System, Computing location using IMU, Dead Reckoning | |
| 8 | Coordinate systems, Earth-centered Earth-fixed coordinate system, Computing location using Global Positioning System, Computing location using IMU, Dead Reckoning | |
| 9 | Depth First Search, Breadth First Search, A* algorithm, Djikstra, Mini-Max, Alpha-Beta, Bug1, Bug2, Tangent Bug, Random Particle Optimization, Additive Attractive/Repulsive potential, Gradient descent | |
| 10 | Depth First Search, Breadth First Search, A* algorithm, Djikstra, Mini-Max, Alpha-Beta, Bug1, Bug2, Tangent Bug, Random Particle Optimization, Additive Attractive/Repulsive potential, Gradient descent | |
| 11 | Depth First Search, Breadth First Search, A* algorithm, Djikstra, Mini-Max, Alpha-Beta, Bug1, Bug2, Tangent Bug, Random Particle Optimization, Additive Attractive/Repulsive potential, Gradient descent | |
| 12 | Sensors for obstacle detection, Dead reckoning navigation, Use of previously detected obstacles for navigation | |
| 13 | Probabilistic estimation, Linear Kalman filtering, Extended Kalman filter | |
| 14 | Probabilistic estimation, Linear Kalman filtering, Extended Kalman filter |
Sources
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | - | - |
| Final Exam/Final Jury | - | - |
| Toplam | 0 | 0 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Gains adequate knowledge of mathematics, physical sciences and the subjects specific to engineering disciplines; gains the ability to apply theoretical and practical knowledge of these areas in the solution of complex engineering problems. | |||||
| 2 | Gains the ability to define, formulate, and solve complex engineering problems; gains the ability to select and apply proper analysis and modeling methods for this purpose. | |||||
| 3 | Gains the ability to design a complex system, process, device or product under realistic constraints and conditions in such a way as to meet the specific requirements; gains the ability to apply modern design methods for this purpose. | |||||
| 4 | Gains the ability to select, and use modern techniques and tools needed to analyze and solve complex problems encountered in engineering practices; gains the ability to use information technologies effectively. | X | ||||
| 5 | Gains the ability to design experiments, conduct experiments, gather data, and analyze and interpret results for investigating complex engineering problems or research areas specific to engineering disciplines. | |||||
| 6 | Gains the ability to work efficiently in inter-, intra-, and multi-disciplinary teams; gains the ability to work individually. | |||||
| 7 | (a) Gains effective oral and written communication skills; gains the ability to write a report properly, understand previously written reports, prepare design and manufacturing reports, deliver influential presentations, give unequivocal instructions, and carry out the instructions properly. (b) Gains the knowledge of, at least, one foreign language; gains the ability to write a report properly, understand previously written reports, prepare design and manufacturing reports, deliver influential presentations, give unequivocal instructions, and carry out the instructions properly in this foreign language. | |||||
| 8 | Gains awareness of the need for lifelong learning; gains the ability to access information, follow developments in science and technology, and adapt and excel oneself continuously. | |||||
| 9 | Gains knowledge about acting in conformity with the ethical principles, professional and ethical responsibility and knowledge of the standards employed in engineering applications. | |||||
| 10 | Gains knowledge of business practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; knowledge of sustainable development. | |||||
| 11 | Gains knowledge of the global and social effects of engineering practices on health, environment, and safety issues, and knowledge of the contemporary issues in engineering areas; gains awareness of the possible legal consequences of engineering practices. | |||||
| 12 | (a) Gains knowledge of (i) fluid mechanics, (ii) heat transfer, (iii) manufacturing process, (iv) electronics and control, (v) vehicle components design, (vi) vehicle dynamics, (vii) vehicle propulsion/drive and power systems, (viii) technical laws and regulations in automotive engineering field, and (ix) vehicle verification tests. (b) Gains the ability to merge and apply these knowledge in solving multi-disciplinary automotive problems. | |||||
| 13 | Gains the ability to make use of theoretical, experimental, and simulation methods, and computer aided design techniques in automotive engineering field. | |||||
| 14 | Gains he ability to work in the field of vehicle design and manufacturing. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | |||
| Prepration of Final Exams/Final Jury | |||
| Total Workload | 0 | ||