CG3- Comunicar-se de manera efectiva oralment i per escrit preparant documents i exposant projectes i resultats amb llengua anglesa CB6- Posseir i comprendre coneixements que aportin una base o oportunitat de ser originals en el desenvolupament i/o aplicació d'idees, sovint en un context d'investigació CB7- Que els estudiants sàpiguen aplicar els coneixements adquirits i la seva capacitat de resolució de problemes en entorns nous o poc coneguts dins de contextos més amplis relacionats amb la seva àrea d'estudi CB8- Que els estudiants siguin capaços d'integrar coneixements i enfrontar-se a la complexitat de formular judicis a partir d'una informació que, sent incompleta o limitada, inclogui reflexions sobre les responsabilitats socials i ètiques vinculades a l'aplicació dels seus coneixements i judicis CE1- Programar, a nivell avançat, en els llenguatges i llibreries més utilitzats en la robòtica de camp intel·ligent CE2- Analitzar un problema relacionat amb sistemes autònoms intel·ligents i identificar les tècniques i les eines apropiades per resoldre'l CE6- Conèixer i saber quan i com utilitzar els principals sensors i actuadors disponibles per a robots de camp intel·ligents CE7- Entendre i ser capaç d'aplicar les principals tècniques de percepció per computador CE8- Comprendre els fonaments matemàtics dels algorismes utilitzats en els sistemes robòtics intel·ligents
Tipus d’activitat Hores amb professor Hores sense professor Hores virtuals amb professor Total Anàlisi / estudi de casos 22,00 82,00 0 104,00 Sessió expositiva 20,00 0 0 20,00 Sessió pràctica 4,00 8,00 0 12,00 Total 46,00 90,00 0 136
Besl, P.J.; McKay, Neil D. (1992). A method for registration of 3D shapes. IEEE transactions on pattern analysis and machine intelligence, 14(), Hartley, R; Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press.
Activitats d'avaluació: Descripció de l'activitat Avaluació de l'activitat % Recuperable Lab Session 1: Stereo Assisting to Lab sessions is MANDATORY. Not complying with this rule may lead to not passing the Lab. Team work, preparation in advance, resolution on time and report writing are key to obtain a good mark. 10 No Lab Session 2: Laser Scanning Assisting to Lab sessions is MANDATORY. Not complying with this rule may lead to not passing the Lab. Team work, preparation in advance, resolution on time and report writing are key to obtain a good mark. 10 No Lab Session 3: Point Set Registration Assisting to Lab sessions is MANDATORY. Not complying with this rule may lead to not passing the Lab. Team work, preparation in advance, resolution on time and report writing are key to obtain a good mark. 10 No Lab Session 4: RGBD Mapping Assisting to Lab sessions is MANDATORY. Not complying with this rule may lead to not passing the Lab. Team work, preparation in advance, resolution on time and report writing are key to obtain a good mark. 10 No Lab Session 5. Neural Rendering Assisting to Lab sessions is MANDATORY. Not complying with this rule may lead to not passing the Lab. Team work, preparation in advance, resolution on time and report writing are key to obtain a good mark. 10 No Class Project During the final weeks of the course, students will undertake a MANDATORY research-based class project on topics related to the contents of the course. Students will be required to prepare and deliver an oral presentation of their findings in class. They should also be prepared to answer questions from the instructor and their peers, demonstrating a thorough understanding of the selected topic. 50 Sí
The minimum mark to pass every evaluated activity is 5. However, averaging between all marks will be considered from a mark of 4 points. Maximum mark is 10. Students must enter the classroom where the assessment activity is to take place with all communication devices — mobile phones, computers, tablets, smartwatches, etc. — switched off and kept inside their backpacks/bags. Failure to comply with this rule will result in a grade of 0 for the activity, as well as the implementation of the actions described in Article 21 of the UdG regulations governing student assessment and grading processes. Students are required to adhere to the University of Girona's Code of Ethics for the Use of Artificial Intelligence. Failure to comply with this code will be regarded as academic fraud and will be subject to the University's disciplinary regulations. If the lecturers determine that academic misconduct or fraud may have occurred, they reserve the right to verify the student's level of knowledge and validate the grade obtained using any assessment method they deem appropriate. Criteris específics de la nota «No Presentat»: If the student does not complete the evaluable laboratory assignments, or does not participate in and complete the class project, he/she will be considered as not having completed the assessment requirements and will be recorded as not presented. Avaluació única: Unique qualification (in case it's required) consists of presenting all lab reports (with evidence of real implementation) and contribute to the oral presentation of the class project. Requisits mínims per aprovar: The subject will be considered as "passed" with a minumum mark of 5.0
Tutorial sessions may be scheduled by contacting the appropriate lecturer by email, depending on the subject or topic for which the student requires academic guidance.