- Department: Electronic Engineering
- Module co-ordinator: Dr. Mark Post
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2020-21
This module will examine collective and emergent behaviour, as observed in natural systems such as ants, fish and other social animals, and the application of those concepts to robotics.
Occurrence | Teaching cycle |
---|---|
A | Autumn Term 2020-21 |
Subject content aims:
Develop technical skills in programming of swarm algorithms applied to robotics
Develop technical skills in the use of simulations
Develop analysis skills in understanding experimental data
Graduate Skills Aims:
Understand data and present it in a meaningful manner
Apply problem solving skills to complex problems
Students should understand, and critically evaluate, a range of swarm robotics approaches
Students should be able to analyse the underlying biological principle of swarm robotics, often where there are competing views of biological understanding
Students should be able to apply a range of swarm intelligence algorithms to a variety of application areas and understand their limitations
Students should be able to critically reflect of the differences in various techniques within the swarm robotics paradigm
Students will be able to combine various swarm robotics approaches to solve a complex problem
Task | Length | % of module mark |
---|---|---|
Essay/coursework Essay |
N/A | 100 |
None
Task | Length | % of module mark |
---|---|---|
Essay/coursework Essay |
N/A | 100 |
Feedback will be via the open assessment, where the students will be provided with a breakdown of comments on various aspects of the assessment, highlighting both strengths and weaknesses of their work.
Russell, Shi & Eberhart - Swarm Intelligence 2001