Study Guide

Objective

The student is able to:
- analyze the need for an autonomous function
- explain the principle of a programmed autonomous system
- define the principles of self-learning systems
- plan and realize simple autonomous robot functions
- create multicore functionality for optimizing performance
- justify the need for autonomous function in a process
- assess the need for parallel processing

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

Materials

System documentation and datasheets

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

Qualifications

Microprocessor technology
Applied electronics