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OPTIMISATION TECHNIQUES

CODE 86733
ACADEMIC YEAR 2021/2022
CREDITS
  • 5 cfu during the 1st year of 10635 ROBOTICS ENGINEERING (LM-32) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR MAT/09
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The Course introduces to optimization models and methods for the solution of decision problems, with particular attention to models and problems arising in Robotics Engineering. It is structured according to the basic topics of problem modelling, its tractability, and its solution by means of algorithms that can be implemented on computers. 

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The lecture presents different theoretical and computational aspects of a wide range of optimization methods for solving a variety of problems in engineering and robotics.

    AIMS AND LEARNING OUTCOMES

    The Course aims at providing the students with the skills required to deal with engineering problems, with particular emphasis on Robotics Engineering, by developing models and methods that work efficiently in the presence of limited resources.

    The students will be taught to: interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making variables, the cost function to minimize (or the figure of merit to maximize), and the constraints; framing the problem within the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.); realizing the "matching" between the solving algorithm (to choose from existing or to be designed) and an appropriate processing software support.

    PREREQUISITES

    Linear algebra. Vector and matrix calculus. Basic mathematical analysis and geometry.

    TEACHING METHODS

    Lectures and exercises. Continuous assessmnet. Attendance recommended.

    SYLLABUS/CONTENT

    Introduction. Optimization and Operations Research for Robotics. Optimization models and methods.

    Linear programming model and algorithms

    Integer linear programming model and  algorithms

    Nonlinear programming model and algorithms

    Graph optimization models and algorithms

    N-stage optimization: dynamic programming model and algorithms

    Putting things together: models, methods, and algorithms for the optimisation of robotic systems

    Software tools for optimization

    Case studies from Robotics

    RECOMMENDED READING/BIBLIOGRAPHY

    Lecture notes provided by the teacher (study material will be available in the official study portal).

    TEACHERS AND EXAM BOARD

    Exam Board

    MARCELLO SANGUINETI (President)

    MAURO GAGGERO

    DANILO MACCIO'

    MASSIMO PAOLUCCI (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Written, if it will be possible to make exams "in presence". Otherwise, the teacher will decide whether the exam via Teams will be written or oral.

    There will be questions on the main concepts explained during the lectures and it will be required to develop models and propose solution algorithms for problems arising in various applicative scenarios of engineering and robotics.

    ASSESSMENT METHODS

    Final exam and maybe continuous assessment (if there will be a continuous assessement, it will cover 30% of the overall evaluation, whereas 70% will be covered by the final exam).

     

    Exam schedule

    Date Time Location Type Notes
    20/12/2021 09:00 GENOVA Scritto Saranno scritti se si ritornerà agli esami completamente in presenza, se invece sarà richiesto di garantire gli esami a distanza il docente farà *solo* esami a distanza, nel qual caso saranno *orali* su Teams.
    11/01/2022 08:00 GENOVA Scritto
    03/02/2022 08:00 GENOVA Scritto
    30/05/2022 09:00 GENOVA Scritto
    28/06/2022 08:00 GENOVA Scritto
    14/09/2022 08:00 GENOVA Scritto

    FURTHER INFORMATION

    The lectures are organized in theory and case-studies from real-world applications. Other  supervised exercises and practice to use of software tools for optimization are available during additional hours with an instructor.