Informatie opleidingsonderdeel

Intelligent Systems for Robotics (4722)
Contact Person :	dr. Nikolaos TSIOGKAS


Coordinating lecturer :	dr. Nikolaos TSIOGKAS

Language of instruction : English

Sequentiality

No sequentiality

Degree programme

Study hours

Credits

P1 SBU

P1 SP

2^nd Chance Exam¹

Tolerance²

Final grade³

Master of Software Systems Engineering Technology

Optional

108

4,0

108

4,0

Yes

Numerical

Learning outcomes

EC	EC2 - The Master of Software Engineering Technology masters the necessary sets of knowledge and skills regarding the design of integrated, resilient software systems and can creatively conceive, plan and implement them as an integrated part of a series of methodologically ordered actions within multidisciplinary projects with a significant research and/or innovation component. [systems thinking]
	DC	DC-M1 - has knowledge of the basic concepts, structures and coherence.
		BC	The student can recognize the basic parts of a robot (sensors/actuators)
	DC	DC-M2 - has insight in the basic concepts and methods.
		BC	The student understands the functionality of the different parts of the robot.
	DC	DC-M6 - can select methods and make calculated choices to solve problems or design solutions.
		BC	The student can select the appropriate sensors for completing a given task.
	DC	DC-M7 - can use selected methods and tools to implement solutions and designs.
		BC	The student can integrate the given sensors/motors into a working robot.
EC	EC3 - The Master of Software Engineering Technology has advanced knowledge and understanding of the principles and applications of software engineering, including software development processes, software architectures and the software life cycle, and can apply them, with an understanding of current technological developments, in complex and practice-oriented problem domains. [software engineering]
	DC	DC-M2 - has insight in the basic concepts and methods.
		BC	The student can design the software architecture for a robot.
	DC	DC-M3 - can recognize problems, plan activities and perform accordingly.
		BC	The student can identify bottlenecks and problems in the robot software.
	DC	DC-M5 - can analyze problems, logically structure and interpret them.
		BC	The student can identify sources of bottlenecks or errors and plan activities to correct them.
	DC	DC-M6 - can select methods and make calculated choices to solve problems or design solutions.
		BC	The student can select appropriate methods for communication among the software components of the robot.
	DC	DC-M7 - can use selected methods and tools to implement solutions and designs.
		BC	The student can implement software methods for component-based robot architectures.
EC	EC5 - The Master of Software Engineering Technology masters the necessary sets of specialised knowledge and skills for the design of modular, integrated software systems that, on the basis of data acquisition and data analysis, can make intelligent decisions and that are resilient (secure, robust and scalable), within multidisciplinary projects with an applied research and/or innovation component. [intelligent & resilient systems]
	DC	DC-M1 - has knowledge of the basic concepts, structures and coherence.
		BC	The student knows basic methods for control, motion planning, robot vision, and task planning.
	DC	DC-M2 - has insight in the basic concepts and methods.
		BC	The student understands the differences among methods, as well as the pros and cons of each method.
	DC	DC-M4 - can gather, measure or obtain information and refer to it correctly.
		BC	The student can measure the performance of each method and interpret the results.
	DC	DC-M6 - can select methods and make calculated choices to solve problems or design solutions.
		BC	The student is able to select appropriate methods and algorithms given the task.
	DC	DC-M7 - can use selected methods and tools to implement solutions and designs.
		BC	The student can implement the appropriate methods and algorithms given the task.
	DC	DC-M8 - can evaluate knowledge and skills critically to adjust own reasoning and course of action accordingly.
		BC	The student can evaluate the performance of the robot in a given task and adjust components/algorithms to improve it.

EC = learning outcomes DC = partial outcomes BC = evaluation criteria

Content

This course focuses on bridging the gap between artificial intelligence and robotic systems. It will explore the basics of a robotic system (sensors, actuators, software etc.), and how artificial intelligence can be used to improve the autonomy of such robotic systems. Part of the course will focus on knowledge representation, reasoning, plannig. In addition, there will be a focus on how one goes from semantic knowledge representations, to actual software implementations that allow the robot to interact with the environment.

Organisational and teaching methods

Organisational methods
	Application Lecture ✔
Teaching methods
	Demonstration ✔
	Discussion/debate ✔
	Exercises ✔
	Homework ✔

Evaluation

Period 1 Credits 4,00

Evaluation method

Written exam

100 %

Case study

✔

Homework

✔

Paper

✔

Oral explanation

✔

Second examination period

Evaluation second examination opportunity different from first examination opprt

Compulsory course material

All material will be distributed through Toledo

Exchange Programme Engineering Technology