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 | Applies knowledge in mathematics, science, and computing to solve engineering problems related to manufacturing technologies. | |||||
| 2 | Analyzes and identifies problems specific to manufacturing technologies. | |||||
| 3 | Develops an approach to solve encountered engineering problems, and designs and conducts models and experiments. | |||||
| 4 | Designs a comprehensive manufacturing system (including method, product, or device development) based on the creative application of fundamental engineering principles, within constraints of economic viability, environmental sustainability, and manufacturability. | |||||
| 5 | Selects and uses modern techniques and engineering tools for manufacturing engineering applications. | |||||
| 6 | Effectively uses information technologies to collect and analyze data, think critically, interpret, and make sound decisions. | |||||
| 7 | Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and necessary organizational skills. | |||||
| 8 | Communicates effectively in both spoken and written Turkish and English. | |||||
| 9 | Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself. | |||||
| 10 | Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of Manufacturing Engineering. | |||||
| 11 | Effectively utilizes resources (personnel, equipment, and costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development. | |||||
| 12 | Considers the health, environmental, social, and legal consequences of engineering practices at both global and local scales when making decisions. | |||||
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 | ||