GRADUATE IN FOOD ENGINEERING
A. COURSE
Name: Robotics Applied to Food Industry
Code GAUSS: 205000189
B. INSTRUCTOR NAMES
Course Coordinator: Pilar Barreiro Elorza
Other: Elvira Martínez Ramírez
Belén Diezma Iglesias
José Álvarez Sánchez
C. CATEGORIZATION OF CREDITS ( ECTS)
Mathematics and Basic Science: 1.75 ECTS
Engineering topics: 1.75ECTS
Other: 0.5
D. TEXT BOOKS (title, author, and year) AND OTHER SUPPLEMENTAL MATERIALS
Barrientos, A. (2007). Fundamentos de robótica. McGraw Hill. ISBN-13: 978-8448156367
Caldwell, D. G. (Ed.). (2012). Robotics and automation in the food industry: Current and future technologies. Ed. Elsevier.
Corke, P. (2017). Robotics, vision and control: fundamental algorithms in MATLAB® second, completely revised (Vol. 118). Ed. Springer.
Glasbey, C. A., & Horgan, G. W. (1995). Image analysis for the biological sciences (Vol. 1). Chichester: Wiley.
Guizard, C., Bellon, V., & Sevila, F. (1992). Vision artificielle dans les industries agro-alimentaires (p. 273). Cemagref Editions.
Naoshi Kondo, K.C.Ting (Editors). (1998). Robotics for Bio Production Systems. ASAE Publication. ISBN-13: 978-0929355948
Naoshi Kondo, Mitsuji Monta, et ál. (2011). Robots: Mechanisms and Practice. 2011.Kyoto University Press. ISBN-13: 978-1920901837
Stork, D. G., Duda, R. O., Hart, P. E., & Stork, D. (2001). Pattern classification. A Wiley-Interscience Publication.
Sun, D. W. (Ed.). (2012). Computer vision technology in the food and beverage industries. Elsevier.
Technical references of robots at the agri-food industry:
Barreiro Elorza, Pilar; Correa Hernando, Eva Cristina; Diezma Iglesias, Belén y Muñoz García, Miguel Angel (2017). El carro de compra inteligente. “Alimentaria” (n. 480); pp. 91-93. ISSN 0300-5755.
Barreiro Elorza, Pilar; Correa Hernando, Eva Cristina; Dacosta Neto, W. y Diezma Iglesias, Belen (2017). Lo que hemos aprendido de la (re)evolución de las 4.0: agricultura, transporte e industria alimentaria. En: “IX Congreso CyTA/CESIA”, 17/05/2017-19/05/2017, Madrid, España. p. 1.
Barreiro Elorza, P., & García-Ramos, F. J. (2006). Actualidad y futuras tendencias en líneas de confección de frutas y hortalizas. Alimentación, equipos y tecnología, 25(209), 47-60.
Barreiro Elorza, P. B., Ruiz-Altisent, M. (2004). Espectrofotómetros para la industria agroalimentaria: avances en el sector hortofrutícola. Alimentación, equipos y tecnología, 23(196), 53-58.
E. SPECIFIC COURSE INFORMATION
E1. Brief description of the content of the course (catalog description)
This subject was created with the aim of describing the most relevant applications of robotics in the different sectors of the agri-food industries. To understand the scope of the subject, it also deals with the physical and mathematical bases that govern the construction and programming of manipulators, as well as the design and configuration of final actuators. It then focuses on the theoretical and applied aspects related to perception systems, with an exhaustive analysis of the application of artificial vision, so that you can apply a wide variety of shape, colour, and texture recognition algorithms.
You will learn to use advanced programming libraries such as Matlab/Octave and you will work in small groups to propose robotisation solutions applied to specific examples of the agri-food product of your choice.
E2.Prerequisites (asignaturas/contenidos): _
E3.Type of course: (required or elective): elective
F.SPECIFIC GOALS OF THE COURSE
F1.Specific Outcomes (CE)
CE03 – Basic knowledge of the use and programming of computers, operating systems, databases and software with engineering applications.
CE20 – Ability to know, understand and use the principles of: Food engineering and technology. Basic food engineering and operations. Food technology. Processes in agri-food industries. Modelling and optimisation. Quality and food safety management. Food analysis. Traceability.
F2.Student Outcomes (SO) ABET
SO2: An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factor.
F3.Transversal Outcomes (CT)
CT01 – Oral and written communication: ability to communicate ideas, problems and solutions to both specialised and non-specialised audiences (EUR-ACE: Sub RA 5.6)
CT02 – Analysis/synthesis and critical reasoning: the ability to critically evaluate arguments, hypotheses, abstract concepts and data, applying scientific and engineering knowledge, in order to make technical judgements and contribute to the solution of complex problems (EUR-ACE: Sub RA 1.1, Sub RA 1.2, Sub RA 1.3, Sub RA 2.2, Sub RA 2.1).
CT05 – Respect for the environment: ability to offer solutions compatible with the conservation of the environment in a responsible and sustainable manner, in order to avoid or reduce the negative effects produced by inadequate practices caused by human activity and to enhance the benefits that the professional activity of engineering can generate in the environmental field. (EUR-ACE: Sub RA 6.1, Sub RA 6.2, Sub RA 8.1, Sub RA 8.2)
CT08 – Teamwork and interpersonal skills: ability to work in an international context, integrating in multidisciplinary and multicultural teams. (EUR-ACE: Sub RA 5.5)
CT10 – Leadership and decision making ability to lead teams, contributing to their personal and professional development, in order to achieve an objective set in their field of study (processes, products, systems, etc.) taking into account social, environmental, economic and industrial constraints, knowing how to choose the best alternative to act and being responsible for the scope and consequences of the option taken (EUR-ACE: Sub RA 3.1, Sub RA 3.2, Sub RA 6.2).
CT12 – Creativity: ability to design a system, component, process or experiment and to solve in an original way situations or problems in the field of engineering. (EUR-ACE: Sub 5.1 Sub RA 6,2, Sub RA 8.1, Sub RA 8.2)
G.BRIEF LIST OF TOPICS TO BE COVERED
Introduction
Geometry of manipulators
Mathematical tools
Robot kinematics
Introduction to Matlab. Robotic and vision Toolboxes
Robot dynamics
Path control
Final drives
Basics of vision/perception systems
B/W image segmentation principle
Colour image segmentation principle
Shape recognition
Texture recognition
Characteristic parameter extraction
Spectral signatures of food products
Spectral image and data analysis
Course project applied to agri-food products.
