About this course

Introduction and history - serial and parallel manipulators. Mobile robots.

Rigid body motion - Rigid body transformations, homogeneous transformations. Rotations in the Euclidean space. Rotation matrices and Euler angles. Transformations in the Euclidean space. Differential kinematics, velocity representations and Jacobians.

Kinematics - forward and inverse kinematics. Manipulator Jacobian and workspaces. Joint space and operational space representations.

Redundant and parallel manipulators. Holonomic and non-holonomic systems.

Dynamics - inertial properties of rigid bodies. Dynamic equations of single body and multibody motion.

Navigation, localization and mapping (SLAM). Sensors and perception. Robot vision. Machine learning in robotics.

Learning outcome

The students will get an introduction and peek into the magic world of Robotics and have acquired knowledge about, and skills in, design, analysis and the application of robots. This includes the combination av practical work and programming, with mathematical modelling of serial, parallel and mobile robots. The students should also have basic knowledge about, and skills in, applying the most used methods for robot vision and machine learning for different applications within robotics.

  • Learning activities

    The course consists of lectures, exercises, computer exercises (the use of programs in analysis and design of robots), simulations and laboratory work.

  • Teaching support

    The teacher is available for consultation in the lecturing period and supervises the practical exercises and is otherwise available by e-mail and direct contact.

  • Syllabus

    "Robotics, Vision and Control", Peter Corke, Springer, 2nd Edition (e-book) + lecture notes.

  • Prerequisites

    MAHT111 Calculus 1, 10 ECTS. MATH112Calculus 2, 10 ECTS. MATH113 Linear algebra and linear differential equations, 10 ECTS. INF120 Programming and data processing, 10 ECTS

  • Assessment method

    All mandatory laboratory work and oral presentation must be approved in addition to written final report which will count 100% towards the final grade (A-E/F).

    Portfolio Karakterregel: Letter grades

  • About use of AI

    Project assignment: K2 - Specified use of AI

    In the project assignment, AI can be used for idea generation and proofreading. The use of AI should be described with a brief explanation of which programs you have used and how the program is used. The student group is responsible for the final content of the report after language proofreading has been completed.

    Descriptions of AI-category codes.

  • Examiner scheme

    The external and internal examiner jointly prepare the exam questions and the correction manual. The external examiner reviews the internal examiner's examination results by correcting a random sample of candidate's exams as a calibration according to the Department's guidelines for examination markings.

  • Mandatory activity

    Mandatory work: laboratory work, oral presentation and written report.

  • Teaching hours

    Lectures, 4 hours per week. Laboratory work.

  • Preferential right

    Applied robotics

  • Admission requirements

    Special requirements in Science