Robot Manipulators and Mobile Robots

Robotkarok és mobilis robotok

A tantárgyleírás hatályossága

Hatályosság kezdete:

2026. March 21.

Hatályosság vége:

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Subject name (Hungarian, English)

Robotkarok és mobilis robotok

Robot Manipulators and Mobile Robots

Subject code

BMEVIIIMA21

Subject type

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Training Level

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Course types and hours (weekly/semester)

Course type	lecture	tutorial	laboratory
hours (weekly)	2	1	0
type (linked/independent)	—	derived course	—

Assessment type

vizsga

Credits

Subject coordinator

Gincsainé Szádeczky-Kardoss Emese

contact: szadeczky.emese@vik.bme.hu

Responsible department

Irányítástechnika és Informatika Tanszék

Faculty

Villamosmérnöki és Informatikai Kar

Subject website

https://edu.vik.bme.hu/

Primary curriculum type

—

Direct prerequisites – Strong prerequisite

none

Direct prerequisites – Weak prerequisite

none

Direct prerequisites – Parallel prerequisite

none

Direct prerequisites – Milestone prerequisite

none

Direct prerequisites – Exclusion

none

Objectives

Programme

Lectures:

Basics of Mechatronics

Repetition and introduction of mathematical relationships and notations. Description of the position and orientation of rigid bodies in the 2D plane and 3D space.

Kinematic models of robotic manipulators

Denavit-Hartenberg form of robotic manipulators. Direct and inverse geometric problem. Jacobi matrix of robots. Redundant and underactuated cases.

Dynamic models of robotic manipulators

Lagrange equation. Dynamic model based on the Lagrange equation. Example with a 2-DoF robot arm.

Control of robotic manipulators

Decentralized 3-loop cascade control. Computed torques method. Hybrid position and force control.

Trajectory planning of robotic manipulators

Path planning task illustrated by the pin-hole problem. Polynomial trajectory planning in joint variables.

Introduction to mobile robotics

Types of mobile robots, mathematical models (wheeled, legged, and flying robots).

Navigation of mobile robots

Presentation of navigation methods. Sensors of the inertial navigation; measured and calculated quantities. Sensing the environment.

Sensor fusion

LS estimation, Kalman filter, Particle filter, formulation of SLAM problem.

Hierarchical implementation of motion planning

Global and local planning methods. Types of maps and applicable planning solutions (deterministic and probabilistic), reactive planners (e.g. APF, Bug, VO methods).

Optimal path planning for a mobile robot in the plane

The optimal path for Dubins and Reeds-Shepp type robots. Continuous curvature path planning.

Coverage path planning

Formulation of coverage planning problems, areas of application, and solutions (random, systematic coverage methods).

Path tracking control of mobile robots

Control based on error transformation, flatness-based control, PI type solutions.

Robot Operating System

Basic of ROS architeckture: ROS 1 and ROS 2 shortly.

Practices:

- Modelling a robot manipulator in Matlab-Simulink-Simscape environment (direct and inverse geometry)

- Control of a robot in Matlab-Simulink-Simscape environment

- Odometry of a differential driven mobile robot

- Inertial navigation (components of IMU and their fusion, advantages, and disadvantages)

- Using Kalman filter for navigation

- Determining the optimal path for Dubins type robots

- Tracking control of a mobile robot

The course aims to summarize the theoretical and practical foundations of modeling, control, and implementation of industrial robot systems and unmanned, autonomous mobile robots. The course presents the most common types of robots, the theoretical basis of their modeling, and the methods of their trajectory planning and control. It gives details about the modern sensors used for the navigation of mobile robots and the basic sensor fusion solutions, as well as different motion planning methods. Students who successfully pass the course can participate in assembling complex robot systems and developing and implementing their control algorithms.

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

Two lectures in a week, and two practices in every two weeks. Concepts and methods based on each other are presented during the semester, so thorough and continuous preparation is recommended to understand the material of the lectures and practices. The Matlab-based development environment used in the practices is available to students on virtual machines during and outside of the practices.

Tanulástámogató anyagok

Online források

Lecture slides and; additional materials on the website of the course. ; LaValle, S. M.:; Planning Algorithms. Cambridge University Press, 2006, ISBN-13: 978-0521862059

Recommended preliminary knowledge for completing the subject

Knowledge type competencies

(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)

Mathematics, Control engineering

Skill type competencies

(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)

nincs

Recommended (non-compulsory) preliminary competencies

(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)

Mathematics, Control engineering

General rules

Requirements: Study period: There are two requirements to get a signature. Students have to satisfy both: 1. Homework: Successful submission of the solution to the individual homework assigned to each student during the semester. (Evaluation: accepted/not accepted). 2. Mid-term exam: One 90-minute-long mid-term exam is written during the semester. Its result must be at least 2 (on a scale of 1 to 5). The topics of the mid-term exam contain half of the entire semester’s topics. The result of the mid-term gives 40 percent in the result of the final exam.Exam period: Obtaining a signature is a condition for admission to the exam. The exam consists of a written test and the inclusion of the result achieved for the mid-term exam. (There is no possibility to improve the result of the mid-term exam during the exam period.) Additional possibilities: The mid-term exam can be repeated (or its result can be improved) once in the teaching period. The mid-term exam cannot be repeated in the retake period. Late submission of the homework is possible in the retake period.

Assessment methods

In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses

Not provided.

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements

Requirements valid from:

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Requirements valid until:

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Curriculum placement

No curriculum placements recorded for this subject version.