Swarm Robotics Competitions

Code:

CSR01

Acronym:

CSR

Keywords
Classification	Keyword
CNAEF	Engineering and related techniques

Instance: 2024/2025 - 2S (edição n.º 1)

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Swarm Robotics Competitions

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
CSR	0	Syllabus	1	-	1,5	12	40,5
L.AERO	0	Syllabus	2	-	1,5	12	40,5
L.BIO	1	Syllabus	2	-	1,5	12	40,5
L.EA	0	Syllabus	1	-	1,5	12	40,5
L.EC	1	Syllabus	1	-	1,5	12	40,5
L.EEC	16	Syllabus	2	-	1,5	12	40,5
L.EGI	0	Syllabus	1	-	1,5	12	40,5
L.EM	12	Syllabus	2	-	1,5	12	40,5
L.EMAT	0	Syllabus	1	-	1,5	12	40,5
L.EMG	0	Plano de estudos oficial a partir de 2008/09	2	-	1,5	12	40,5
L.EQ	0	Syllabus	2	-	1,5	12	40,5

Teaching language

Portuguese

Objectives

• provide students with basic skills in the design, development and implementation of simple algorithms for cooperation and consensus of multiple agents with a special focus on robotic swarm competitions. Swarm intelligence and control algorithms have applications in many scientific fields, including, environmental, bioengineering, civil, data science, computer and information science, electrotechnics, physics, mechanics, nanotechnology and chemistry; what makes this topic cross-cutting.


• prepare students for the robotics swarm competition, promoting the development of complementary skills (soft skills), namely: teamwork, cooperation, peer communication, time management, resource management, stress management.

Learning outcomes and competences

• Explanation of the basic functioning of simple consensus algorithms and swarm intelligence between agents;


• Development of small Python program modules on the Notebook platform for the competition that consists of using mobile robots to search and collect the greatest number of resources in a fixed period of time.


• Development of soft skills in the areas of teamwork, cooperation, peer communication, time management, resource management, stress management

Working method

Presencial

Program

• Introduction to multi-agent cooperation and consensus algorithms.


• Introduction to distributed architectures of robotics swarm systems for formation and coverage control tasks.


• Introduction to Notebook python.


• Development, implementation, and simulation of robotics swarm programs aiming to achieve the maximum score in the competition.

Mandatory literature

Mehran Mesbahi; Graph theoretic methods in multiagent networks. ISBN: 978-0-691-14061-2
Hamann, H; Swarm robotics: A formal approach , Springer, 2018

Teaching methods and learning activities

The teaching-learning methodology is based on the non-requirement of any prior knowledge or competence on the part of students in programming languages, algorithms and hardware associated with mobile robotics systems, and is therefore suitable for any undergraduate or Master's degree.

According to the program, the classes comprise theoretical and laboratory typologies. The theoretical part consists of lectures to expose the subjects to be dealt with, accompanied by examples and demonstrations. The laboratory part is focused on application work, namely the development of robotics swarm programs, simulation and competition testing. At this stage, students will have the possibility to apply and test the acquired knowledge.

 

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	15,00
Trabalho laboratorial	85,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	28,50
Frequência das aulas	12,00
Total:	40,50

Eligibility for exams

active participation in the proposed activities

Calculation formula of final grade

Two components will be considered:

• ETI - Exercises proposed as Individual Work


• TL - Laboratory Work

Final grade calculation formula = 15% ETI + 85% TL