| Code | DSP721 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Name | Modern robot systems | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Status | Compulsory/Courses of Limited Choice | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Level and type | Undergraduate Studies, Academic | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Field of study | Computer Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Faculty | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Academic staff | Agris Ņikitenko | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Credit points | 3.0 (4.5 ECTS) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Parts | 1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Annotation |
Modern robot systems architecture key aspect is the relationship between software solutions and mechanical solutions. Flexibly linking software and mechanical solutions it is possible to build robot system that can be easily modified and its parts can be reused. The course covers robots architectures and methods how to use different logics, mathematical formalisms and algorithms for planning. Significant attention will be paid to swarm intelligence solutions.. The course practical part is organized as classroom courses where students will develop computer programs in the Microsoft Robotics Studio environment to solve problems covered in theoretical lectures.. |
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| Contents |
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Goals and objectives of the course in terms of competences and skills |
The aim is to provide knowledge of robot architectures, planning methods, and theories necessary for their analysis. Tasks: 1) Be able to use different logics and mathematical formalisms for planning and realization of communication. 2) Be able to use evolutionary computation and the swarm intelligence to solve various problems. 3) Be able to analyze the robot architectures. 4) Be able to analyze and implement control in distributed robot systems. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning outcomes and assessment |
Be able to work with a variety of mathematical formalisms, for example., situation calculus, event calculus, lambda calculus, pi calculus, etc. - Practical works 1, 2 and 3. Exam questions Know the principles of rule based systems and how to apply these knowledges in planning in robotics. - Practical works 4, 5 and 6. Exam questions Be able to use optimization methods - Practical works 7, 8 and 9. Exam questions Be able to use evolutionary computation methods - Practical works 10, 11 and 12. Course assignment. Exam questions Be able to use different a swarm intelligence methods - Practical works 13, 14, 15 and 16. Course assignment. Exam questions Be able to implement control in distributed robot systems - Practical works 17 and 18. Exam questions Be able to analyse different robot architectures - Practical works 19 and 20. Exam questions |
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| Evaluation criteria of study results |
Report of independent work - 75%
Final exam - 25% |
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| Course prerequisites | Mathematics, Programming fundamentals | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course planning |
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