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 Coordinator
Course Lecturer(s)
Course Assistants
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 An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems
2 An ability to define and analyze issues related with manufacturing technologies
3 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
4 An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints
5 An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications
6 An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly
7 An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually
8 An ability to attain efficient communication skills in Turkish and English both verbally and orally
9 An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology
10 An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering
11 An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development
12 An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process

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