CODE 86733 ACADEMIC YEAR 2021/2022 CREDITS 5 cfu anno 1 ROBOTICS ENGINEERING 10635 (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 MARCELLO SANGUINETI Ricevimento: By appointment. Exam Board MARCELLO SANGUINETI (President) MAURO GAGGERO DANILO MACCIO' MASSIMO PAOLUCCI (President Substitute) LESSONS LESSONS START https://corsi.unige.it/10635/p/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy 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 Data appello Orario Luogo Degree type Note 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.
