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EEE 547

Introduction to Robotics

This graduate course treats a robot as a rigid-body system you can model, command, and close a sensing loop around, working from kinematic chains and dynamics up through trajectory planning and the perception stack that lets mobile platforms localize and avoid obstacles. Expect to spend most of your time deriving D-H parameters, solving inverse kinematics by hand, grinding through Lagrange-Euler and Newton-Euler equations of motion, and producing two mini-projects alongside the midterm and final. It assumes you are comfortable with linear algebra, dynamics, and signals, and serves as the foundation students lean on before moving into control, motion planning, or SLAM-flavored research.

Kredi3ECTS5FakülteFaculty of EngineeringBölümElectrical and Electronics EngineeringKoordinatörBillur Barshan

→ STARS müfredatı (resmi syllabus)

Haftalık müfredat 14 hafta

Hafta 114–20 Eyl

Robot manipulator terminolojisi ve kinematiğe giriş

nomenclature, robot manipulators, kinematic chain, links, joints, actuators, degrees of freedom, overview of forward and inverse kinematics

robot manipulatordegrees of freedomforward kinematicsinverse kinematics

Hafta 221–27 Eyl

Rigid body ve matrix transformation'lar

rigid body transformations, properties of matrix transformations, translational, rotational, and scaling transformations, composite transformations

rigid body transformationsmatrix transformationstranslation, rotation, scalingcomposite transformations

Hafta 328 Eyl – 4 Eki

Dönme merkezi ve homojen dönüşüm matrisleri

center of rotation, Euler angles, homogeneous coordinates and transformations, composite homogeneous transformation matrix

center of rotationEuler angleshomogeneous coordinatescomposite homogeneous transformation matrix

Hafta 45–11 Eki

Denavit-Hartenberg gösterimi ve arm matrix

Denavit-Hartenberg representation (links, joints, and their parameters), arm matrix

Denavit-Hartenberglinkjointarm matrix

Hafta 512–18 Eki

Kinematic chain örnekleri ve forward kinematics

kinematic chain examples, forward kinematics equations

kinematic chainforward kinematics

Hafta 619–25 Eki

Inverse manipulator kinematics çözüm yöntemleri

inverse manipulator kinematics: methods of solution (numerical, closed-form)

inverse kinematicsnumerical solutionclosed-form solution

Hafta 726 Eki – 1 Kas

Algebraic-geometric çözümler ve workspace

examples to algebraic and geometric solutions, workspace (dexterous, reachable), 6-DOF manipulator solution

algebraic solutiongeometric solutionworkspace6-DOF manipulator

Hafta 82–8 Kas

Velocity kinematics ve Jacobian

velocity kinematics and the Jacobian, derivation of the Jacobian, linear and angular velocity Jacobians, singularities

velocity kinematicsderivation of the Jacobianlinear ve angular velocity Jacobiansingularities

Hafta 99–15 Kas

Ara Sınav

Midterm Exam

Hafta 1016–22 Kas

Robot arm dynamics ve Lagrange-Euler formülasyonu

robot arm dynamics, forward and inverse dynamics, equivalent formulations, Lagrange-Euler formulation, equations of motion

forward dynamicsinverse dynamicsLagrange-Euler formulationequations of motion

Hafta 1123–29 Kas

Robot kol dinamiği: Newton-Euler ve d'Alambert

Newton-Euler and Generalized d'Alambert formulations to robot arm dynamics, forward and backward recursive equations

Newton-EulerGeneralized d'Alambertrobot arm dynamicsrecursive equations

Hafta 1230 Kas – 6 Ara

Trajectory planlaması ve quaternion gösterimi

planning of manipulator trajectories, knot points, bounded deviations method, cubic spline trajectory, quaternions

knot pointsbounded deviations methodcubic spline trajectoryquaternions

Hafta 137–13 Ara

Sensörlere giriş ve mesafe ölçüm yöntemleri

introduction to sensing, classification of sensors, time-of-flight range sensors (ultrasonic, laser-based ranging), triangulation systems, proximity sensing (inductive, capacitive, magnetic, ultrasonic, optical)

sensor classificationtime-of-flighttriangulationproximity sensing

Hafta 1414–20 Ara

Dead-reckoning ve multi-sensor data fusion

dead-reckoning systems (odometry, potentiometers, optical encoders, inertial sensing: gyroscopes, accelerometers, tilt sensors), multi-sensor data fusion

odometryoptical encodersinertial sensingmulti-sensor data fusion

Değerlendirme 100% · 4 adım

40%

10%

Midterm Midterm Exam 40%

Final Final Exam 40%

Quiz Quiz 1, Quiz 2 20%

en büyük tek kalem %40 · sınav ağırlığı %100 · 15 dönem ortalaması 2.94 (76 öğrenci) nasıl hesaplanıyor

Önerilen kaynaklar 3 kitap

📕

Zorunlu

Robot Modeling and Control

M.W. Spong, S. Hutchinson

and M. Vidyasagar · 2006

📖

Önerilen

Introduction to Robotics

Analysis, Sytems

Applications · S. B. Niku

📖

Önerilen

Robotics: Control

Sensing, Vision

and Intelligence · K.S. Fu

Bu dersi alınca · 8 öğrenme çıktısı

understanding rigid body transformations, properties of matrix transformations, basic translational, rotational, and scaling transformations, formulating homogeneous transformation matrices and composite transformations.Midterm Exam, Final Exam, Quiz 1, Quiz 2
understanding Denavit-Hartenberg (D-H) representation and identifying D-H parameters; deriving the forward kinematics equations of a robot arm; being able to design more complicated kinematic chains based on simple and basic modules such as the 3-D wrist and the planar arm.Midterm Exam, Final Exam, Quiz 1, Quiz 2
determining the existence and the number of inverse kinematics solutions for a robot arm; finding inverse kinematics solutions of manipulators analytically and geometrically.Midterm Exam, Final Exam, Quiz 1, Quiz 2
deriving equations of motion (dynamics equations) of manipulators using two equivalent approaches (Lagrange-Euler and Newton-Euler methods).Midterm Exam, Final Exam, Quiz 1, Quiz 2
mastering different approaches to trajectory planning in Cartesian and joint spaces.Midterm Exam, Final Exam, Quiz 1, Quiz 2
learning the characteristics and principles of operation of different sensing modalities for robots and applications of sensing in the field of robotics.Midterm Exam, Final Exam, Quiz 1, Quiz 2
acquiring working knowledge of numerical tools such as MATLAB Robotics Toolbox.Quiz 1
using numerical tools to formulate and solve robotics problems with computers.Quiz 1

Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.

🤖 GenAI politikası

The use of Generative Artificial Intelligence (GenAI) tools such as ChatGPT, Gemini, or DeepSeek is strictly prohibited during in-class assessments, including the midterm exam, final exam, quiz, and survey & in-class presentation.

Ders notları · henüz yok

EEE 547 için defter ekibi henüz not yazmadı.

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Geçmiş GPA dağılımı 15 dönem · ort. 2.94

Dönem	Course CPA
2023-2024 Fall	3.00	1 sec · 2 öğr
2022-2023 Fall	3.10	1 sec · 4 öğr
2021-2022 Fall	1.30	1 sec · 2 öğr
2020-2021 Spring	1.60	1 sec · 4 öğr
2018-2019 Fall	3.22	1 sec · 6 öğr
2017-2018 Fall	2.68	1 sec · 4 öğr
2016-2017 Fall	3.40	1 sec · 5 öğr
2015-2016 Fall	4.00	1 sec · 2 öğr
2014-2015 Fall	3.85	1 sec · 2 öğr
2012-2013 Fall	3.22	1 sec · 6 öğr

Aggregate course GPA · Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz. Tüm derslerin ortalamaları →

2026-2027 Güz için şubesi henüz görünmüyor. Kayıt sistemi bu dersi bu dönem listelemiyor, ama şube girişi sürüyor: ders kaydı 15 Eylül, bölümler o güne kadar şube ekleyebiliyor. Son 4 güz döneminin 2 tanesinde açılmış; her yıl açılan bir ders değil. Ön kayıt müfredat üzerinden yapılıyor, açılan şube listesi üzerinden değil; o yüzden ön kayıtta seçebildiğin bir dersin şubesi burada henüz görünmeyebilir. Kesin sonuç ders kaydında belli oluyor. kayıt tarihleri → · açık dersler

⚠️ FZ engelleyen şartlar

To attend the midterm exam and get a grade >=32.

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Billur Barshan