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CourseAerospace, UAM, Drones & Space Applications open

Swarm drone course (cooperation)

Online course of 300 hours in swarm drones (cooperation): 6 modules covering aerial multi-agent systems, formation modelling and control, task planning and allocation, cooperative communications and perception, and implementation and operation, with an assigned technical tutor.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Understand the principles of collective behaviour in aerial systems.

02

Apply consensus and formation control algorithms.

03

Implement distributed task allocation methods.

04

Design communication architectures for swarms.

05

Simulate cooperative missions in realistic environments.

06

Assess robustness against agent and link failures.

07

Analyse the regulatory and ethical implications of swarms.

Profile

Who it's for

  • Multi-agent systems engineers.
  • Swarm robotics researchers.
  • Developers of coordination algorithms for aerial fleets.
  • Aerial communications engineers working on FANETs and mesh links.
  • Drone show operators.
  • Robotics simulation specialists.
  • Engineering or science graduates seeking to enter research and academia.
Standards & tools

What you'll work with

  • Implementing Regulation (EU) 2019/947 (rules and procedures for UAS operations)
  • Delegated Regulation (EU) 2019/945 (UAS product requirements)
  • Implementing Regulation (EU) 2021/664 (U-space) (regulatory framework for U-space)
  • IEEE 802.11s (wireless mesh networking)
  • Regulation (EU) 2021/821 on dual-use items (EU export control of dual-use items)
  • Regulation (EU) 2018/1139 (EU common rules in civil aviation and EASA)
  • ROS 2
  • Gazebo
  • Crazyswarm2
  • ns-3
  • PX4 Autopilot
Syllabus

6 modules

  1. 1.1Swarm concepts and collective behaviour
  2. 1.2Biological inspiration: flocks and colonies
  3. 1.3Centralised and decentralised architectures
  4. 1.4Homogeneous and heterogeneous agents
  5. 1.5Communication topologies
  6. 1.6Scalability and robustness
  7. 1.7Civil applications of swarms
  8. 1.8Drone light shows
  9. 1.9Open research challenges
  10. 1.10Case study: analysis of a 1,000-drone show
  1. 2.1Applied graph theory
  2. 2.2Consensus algorithms
  3. 2.3Reynolds model: separation, alignment and cohesion
  4. 2.4Leader-follower formation control
  5. 2.5Virtual structures
  6. 2.6Artificial potential fields
  7. 2.7Inter-agent collision avoidance
  8. 2.8Velocity obstacles and ORCA
  9. 2.9Formation stability
  10. 2.10Practical exercise: simulating the Reynolds model in Python
  1. 3.1Auction-based task allocation
  2. 3.2The CBBA algorithm
  3. 3.3Area coverage by multiple agents
  4. 3.4Distributed search
  5. 3.5Multi-agent trajectory planning
  6. 3.6Particle swarm optimisation
  7. 3.7Multi-agent reinforcement learning
  8. 3.8Replanning after loss of agents
  9. 3.9Cooperative performance metrics
  10. 3.10Lab: task allocation with CBBA
  1. 4.1Flying ad hoc networks (FANET)
  2. 4.2Routing protocols
  3. 4.3Latency and packet loss
  4. 4.4Relative localisation with UWB
  5. 4.5Distributed data fusion
  6. 4.6Collaborative SLAM
  7. 4.7Time synchronisation
  8. 4.8Security in swarm communications
  9. 4.9Middleware: ROS 2 and DDS
  10. 4.10Workshop: simulated communications network with ns-3
  1. 5.1Simulators: Gazebo and AirSim
  2. 5.2Crazyflie and research platforms
  3. 5.3Deployment with PX4 and MAVROS
  4. 5.4Multi-vehicle ground control stations
  5. 5.5Requirements for operating several drones per pilot
  6. 5.6Risk assessment of swarm operations
  7. 5.7Ethics and dual-use applications
  8. 5.8Indoor testing with motion capture
  9. 5.9Scaling from simulation to real flight
  10. 5.10Case study: cooperative coverage for area search
  1. 6.1Defining the cooperative mission
  2. 6.2Architecture selection
  3. 6.3Coordination algorithm design
  4. 6.4Communications design
  5. 6.5Implementation in a simulator
  6. 6.6Tests with agent failures
  7. 6.7Metrics evaluation
  8. 6.8Proposal for real-world deployment
  9. 6.9Report and presentation
  10. 6.10Final project: final review with your tutor
Methodology, software & labs

How you'll learn

Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, making it compatible with work.

Each module combines theory, guided reading of the original regulations and applied exercises. The working software is ROS 2, Gazebo, Crazyswarm2, ns-3 and PX4 Autopilot, in free, academic or evaluation versions. Exercises are based on real procedures for simulating multi-agent formations, indoor tests with motion capture and robustness tests against loss of agents. Assessment is continuous by module, plus the final capstone project.

Standards covered: Implementing Regulation (EU) 2019/947, Delegated Regulation (EU) 2019/945, Implementing Regulation (EU) 2021/664 (U-space), IEEE 802.11s, Regulation (EU) 2021/821 on dual-use items, Regulation (EU) 2018/1139.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Implementing Regulation (EU) 2019/947, Delegated Regulation (EU) 2019/945 and Implementing Regulation (EU) 2021/664 (U-space) applied to real cases, not as a theoretical annex.
Industry software:ROS 2, Gazebo, Crazyswarm2, ns-3 and PX4 Autopilot, in free, academic or evaluation versions.
Final project:a cooperative drone swarm mission designed, implemented in a simulator and tested against agent failures, with a proposal for real-world deployment, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Reynolds model simulation
Simulate the Reynolds flocking model in Python.
CBBA task allocation
Allocate tasks across a swarm with the CBBA algorithm.
Swarm network simulation
Simulate the swarm communications network with ns-3.
Cooperative area search
Plan cooperative coverage of an area for search missions.
Final project
Design and simulate a cooperative swarm mission, test it against agent failures and propose a real deployment, reviewed by your tutor.
Career opportunities

Where this takes you

Multi-agent systems engineerSwarm robotics researcherCoordination algorithm developerAerial communications engineerDrone show operatorRobotics simulation specialist
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ROS 2, Gazebo, Crazyswarm2, ns-3 and PX4 Autopilot, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

No prior qualification is required; you need an internet connection, a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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