Study Guide

Objective

Is familiar with the basic concepts of intelligent systems and neural networks.
Understand the principles of self-learning systems and master relevant concepts regarding autonomous robots.

Content

Robot construction (hardware)
Implementation of autonomous software
Selection of sensors
Optimization of implementation (software and sensors)
Functionality tests
Demonstrations and final tests
Documentation of the implementation and selected algorithms

Location and time

w. 43-50

Materials

Hardware Specific Documentation (Propeller Tutorials)

Teaching methods

Group work and demonstrations
Documentation of algorithms
Documentation of implementation

Exam schedules

Course examination 3 weeks after completion of the course.
Test of autonomous functions.
Submission of documentation according to given deadlines.

Completion alternatives

No alternative methods of performance. Requires laboratory equipment.

Student workload

The laboratory task and its documentation is carried out mainly as group work, also outside lecture hours.

Further information

w. 43-50: Two test occasions and a final test.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

Is familiar with the basic concepts of intelligent systems
Understands nonlinear systems and neural networks
Understands the principles of self-learning systems
Master relevant concepts in autonomous robots

Assessment criteria, good (3)

Is well versed in the basic concepts of intelligent systems
Can utilize neural networks for technical modeling
Is familiar with several different methods for implementing self-learning systems
Can formulate, structure and report a relevant problem regarding autonomous systems or robots

Assessment criteria, excellent (5)

Owns a deep insight into the basic concepts of intelligent systems and can speculate on future challenges and opportunities
Can perform demanding technical modeling using neural networks
Can program and simulate self-learning systems
Can successfully complete, report and structure a project on autonomous systems or autonomous robots

Assessment criteria, fail (0)

The assessment is made on the basis of submitted documentation and the result of the final test occasion.

Qualifications

Microprocessor technology
Applied electronics