ࡱ> supqrW ybjbjJJD(a(a8&nn)))8a ,) A$L$"$$$%%%$J%%%%%$$Ħ111%$$1%11A$` ʇ+R\ڦ0 $n+PAA %%1%%%%%-%%% %%%%%%%%%%%%%nm : NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE Machines and equipment design department APPROVED Faculty of design and engineering 10 June 2025 CURRICULUM OF ACADEMIC DISCIPLINE ARTIFICIAL INTELLIGENCE SYSTEMS (title) Field of knowledge: G "Engineering, production and construction" Specialty: G11 "Mechanical Engineering (by specializations)" Academic programme "Robotic systems and complexes of agricultural production" Orientation of the program: educational and professional Faculty of design and engineering Developed by: doctor of technical sciences, professor Romasevych Y.O., candidate of technical sciences, docent Krushelnytskyi V.V. Kyiv 2025 Description of the discipline Artificial intelligence systems (title) Studying the course AI Systems in Robotics is essential for understanding how artificial intelligence enhances robotic capabilities. AI enables robots to perceive environments, learn from data, make intelligent decisions, and adapt to changing conditionscrucial features in applications like autonomous navigation, object manipulation, and human-robot interaction. This course provides a foundation in machine learning, computer vision, natural language processing, and decision-making algorithms as applied to robotics. Mastery of AI systems empowers students to design smarter, more autonomous robots for industries such as healthcare, manufacturing, logistics, and exploration. With AI rapidly transforming robotics, this knowledge is vital for innovating future technologies, ensuring efficiency, adaptability, and collaboration between humans and machines in real-world tasks. Area of knowledge, specialty, academic programme, academic degreeAcademic degree MasterSpecialty G11 Mechanical Engineering (by specializations)Academic programmeRobotic systems and complexes of agricultural productionCharacteristics of the disciplineTypeMandatoryGeneral volume of hours180Number of credits ECTS 6Number of modules4Course projectYesControl formExam, testIndicators of the discipline for full-time forms of university studyYear of study1Term1, 2Lectures60 h.Practical classes and seminars-Laboratory classes60 h.Self-study30 h.Number of hours per week for full-time students4 h. 1 semester 4 h. 2 semester 1. Aim, competences and expected learning outcomes of the discipline Artificial intelligence systems (AIS) play an extremely important role in the development of robotics. They give robots the ability to adapt to new situations, learn from data, and make decisions, allowing them to become more efficient, flexible, and useful in a variety of fields, including agriculture. Here are a few aspects of the importance of artificial intelligence systems in the field of robotics: 1) autonomy and understanding of the environment (AIS help robots understand their environment by processing sensory data such as video, audio and touch sensors. This allows robots to learn and make decisions based on the collected data); 2 recognition of objects and planning of actions (AIS allows robots to recognize objects, people and other robots in their environment. The goal of the discipline is the formation of theoretical understanding and practical skills in the development of SSI and their application in the field of robotics. The tasks of the discipline consist in teaching: the main theoretical principles on which SSI is based, their application to the development of control systems for the movement of robots, planning their trajectory, processing sensory information, etc.; the use of software for the implementation of AIS in the field of robotics. Competences acquired: integral competence: the ability to solve complex tasks and problems of industrial mechanical engineering, which involve research and/or innovation and are characterized by uncertainty of conditions and requirements. general competences (GC): GC3. Ability to create new equipment and technologies in the field of mechanical engineering. GC 5. Ability to develop and implement plans and projects in the field of industrial mechanical engineering and related activities, to carry out relevant entrepreneurial activities. GC 6. Ability to design, research and use robotic systems and complexes to meet the needs of agricultural production. GC 7. Ability to use intelligent technologies to ensure the sustainable development of robotic systems for agricultural production. special (professional) competencies (SC): SC1. Ability to apply information and communication technologies. SC 3. Ability to search, process and analyze information from various sources. SC 6. Ability to generate new ideas (creativity). SC 8. Ability to make informed decisions. Expected learning outcomes (ELO): ELO02. Knowledge and understanding of mechanics and mechanical engineering and the prospects for their development. ELO08. Knowledge of the production advantages and features of the use of robotic systems and complexes in the agricultural sector. Programme and structure of the discipline Modules and topicsNumber of hoursfull-timetotalincludinglplabinds.stweeks12345678Module 1. Basics of ANN architectures and their application in roboticsTopic 1. Introduction. Basic concepts and applications of ANNs for robotics114-4-31-3Topic 2. Mathematical basics of ANN operation126-4-23-6Topic 3. Special ANNs for robots155-7-36-8Total for module 13815-15-8-Module 2. Approaches to ANN trainingTopic 4. ANN training "with a teacher" and correspond robotics problems155-7-38-10Topic 5. ANN training "with reinforcement" and correspond robotics problems155-8-211-13Topic 6. Backpropagation method75---213-15Total for module 23715-15-7-Module 3. Development of fuzzy systems for robotics problemsTopic 7. General concepts of fuzzy logic and its use145-5-41-4Topic 8. Development of fuzzy control systems for robot system movement2410-10-44-8Total for module 33815-15-8Module 4. Application of artificial intelligence systems in roboticsTopic 9. Planning the trajectories of the movement of robotic systems64---28-9Topic 10. Synthesis of optimal neurocontrollers of robot link movement186-9-310-13Topic 11. Prediction models of time series for development of mathematical models of robots135-6-213-15Total for module 43715-15-7-Coursework30------Total hours180606030- 3. Topics of lectures !TopicHours1Topic 1. Introduction. Basic concepts and applications of ANNs for robotics42Topic 2. Mathematical basics of ANN operation63Topic 3. Special ANNs for robots54Topic 4. ANN training "with a teacher" and correspond robotics problems55Topic 5. ANN training "with reinforcement" and correspond robotics problems56Topic 6. Backpropagation method57Topic 7. General concepts of fuzzy logic and its use58Topic 8. Development of fuzzy control systems for robot system movement109Topic 9. Planning the trajectories of the movement of robotic systems410Topic 10. Synthesis of optimal neurocontrollers of robot link movement611Topic 11. Prediction models of time series for development of mathematical models of robots5 4. Topic of laboratory (practical, seminars) classes !TopicHours1Preliminary data preparation42Creation of a classifier based on a perceptron23Multilayer perceptron24Study of recurrent ANNs76Classification indicators for assessing the quality of forecasts47Cross-validation to assess the quality of the classifier38Evaluation of the quality of models in classification tasks49Methods of cross-validation410Operations on fuzzy sets611Fuzzification and defuzzification in a fuzzy controller212Fuzzy control system713Synthesis of optimal neurocontrollers for linear systems (robot drive)414Synthesis of optimal neurocontrollers for nonlinear systems (robot link)515Research of predictive properties of recurrent ANNs on time series for development of mathematical models of robots6 Topics of self-study !TopicHours1Analysis of areas of application of ANNs in robotics and agricultural production32Mathematical operations in ANNs23Recurrent ANNs34Preparation of data for training ANNs according to the with teacher paradigm35Application of gradient-free optimization methods for training ANNs according to the reinforcement paradigm26Generalization of the method of backpropagation of the error (the case of multilayer ANNs)27The properties of membership functions and the concept of term in fuzzy logic48Building a rule base for fuzzy control systems49Solving the problem of planning the optimal trajectory of the manipulator robot210Approaches to the synthesis of optimal neurocontrollers of underactuated robotic systems311Prediction models of time series2 6. Methods of assessing expected learning outcomes: When teaching this discipline, the following diagnostic tools are used: oral interview; exam; module tests; defense of laboratory work. 7. Teaching methods: When teaching this discipline, the following methods are used: problem-based learning method; practice-oriented learning method; research-based learning method; educational discussions and debates method; teamwork and brainstorming method. Results assessment The knowledge of a higher education applicant is assessed on a 100-point scale, which is translated into a national assessment in accordance with the current "Regulations on Examinations and Tests at the Vlog of Ukraine." Distribution of points by types of educational activities Type of training activitiesResults teachingEvaluationModule 1. Basics of ANN architectures and their application in roboticsLab 1ELO2, ELO8. To know the basics of ANN and their structures. To be able to prepare develop proper ANN structures for different robotics problems.5Self-study work 15Lab 210Lab 310Lab 410Self-study work 210Lab 510Self-preparation work 310Module 1 test-30Overall on 1st module-100Module 2. Approaches to ANN trainingLab 6ELO2, ELO8. To know the basics of ANN training procedures. To be able to train ANN with different approaches.10Self-study work 410Lab 710Lab 810Lab 910Self-study work 510Self-study work 610Module 2 test-30Overall on 2nd module-100Class work-(1+2)/2*0,7 d"70Test-30Overall for 1st semester-(Class work+Exam)d"100Module 3. Development of fuzzy systems for robotics problemsLab 10ELO2, ELO8. To know the fuzzy-control systems structures. To be able to develop fuzzy-control systems for robots.15Self-study work 715Lab 1115Lab 1215Self-study work 810Module 3 test-30Overall on 3rd module-100Module 4. Application of artificial intelligence systems in roboticsLab 13ELO2, ELO8. To know how to reduce robotic problem to a problem of ANN training. To be able to solve the problems of robots trajectory planning, ANN-control etc.Self-study work 9Lab 14Self-study work 10Lab 15Self-study work 11Module 4 test-30Overall on 4th module-100Class work-(1+2)/2*0,7 d"70Exam-30Overall for 2nd semester-(Class work+Exam)d"100 8.2 Scale for assessing student s knowledge Students rating, pointsNational grading (exam/credits)90-100excellent74-89good60-73satisfactory0-59unsatisfactory 8.3 Assessment policy Deadlines and exam retaking rules works that are submitted late without valid reasons will be assessed with a lower grade. Module tests may be retaken with the permission of the lecturer if there are valid reasons (e.g. a sick leave). Academic integrity rulescheating during tests and exams is prohibited (including using mobile devices). Term papers and essays must have correct references to the literature usedAttendance rulesattendance is compulsory. For good reasons (e.g. illness, international internship), training can take place individually (online by the faculty deans consent) Teaching and learning aids Course on E-learn: https://elearn.nubip.edu.ua/course/view.php?id=5360 lecture notes and their presentations (in electronic form); methodological materials for studying the academic discipline for higher education students. 10. Recommended sources of information Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat Dutta. Intelligent Control of Robotic Systems. 2020. 1st Edition. CRC Press. 674 p. Mellal M. A. (2022). Design and Control Advances in Robotics. IGI Global. 387 p. Zhang C., Wu J., Li C. (2024). Recent Progress in Robot Control Systems: Theory and Applications. MDPI. 312 p. Vladareanu L., Yu H., Wang H. Feng, Y. (2023). Advanced Intelligent Control in Robots. MDPI. 452 p. Gu J., Hu F., Zhou H., Fei Z., Yang E. (2024). Robotics and Autonomous Systems and Engineering Applications of Computational Intelligence. Springer. 434 p. "5@59:>2AL:89 .., CHCT2 .., "5@59:>2AL:0 .. (BCG=V =59@>==V <5@56V: 107>2V ?>;>65==O. 8W2, 2022. 123 A. 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