Postgraduate DiplomaDefense, Military Engineering & Land Systems Applications open

Diploma in Swarm Robotics Algorithms in Defence

Online postgraduate diploma of 800 hours in swarm robotics algorithms for defence. 8 modules covering swarm intelligence, consensus and formation control, task allocation and optimisation, collective perception and localisation, swarm communication networks, multi-agent reinforcement learning, and ethics, law and human oversight (DoD Directive 3000.09, NATO AI principles), with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma

Diploma in Swarm Robotics Algorithms in Defence — studying from New Zealand

New Zealand's defence engineering sector is centred on the New Zealand Defence Force, which operates land, sea and air capabilities, with major bases at Linton and Burnham. Defence procurement and sustainment are managed through the Ministry of Defence, and local firms support maintenance, communications and vehicle systems. Land systems engineering is closely linked to the country's agricultural and forestry sectors, where ruggedised vehicles and equipment are developed and maintained.

Fees for New Zealand: NZ$2,550 (New Zealand dollar), with instalments and employer sponsorship available.

Relevant bodies in New Zealand

  • New Zealand Defence Force (NZDF)
  • Ministry of Defence
  • Defence Industry New Zealand

Typical roles

  • Military Systems Engineer
  • Land Systems Engineer
  • Defence Procurement Specialist
  • Sustainment Engineer

Questions from New Zealand

How is defence and military engineering recognised in New Zealand?

Engineering registration is handled by Engineering New Zealand, and defence procurement and security clearances are managed through the New Zealand Defence Force and government procurement processes. Security clearance requirements can matter more than academic credentials for some roles. SEIUM is a private institute, and its qualifications are not accredited by a national body, so recognition rests with the employer.

What defence and land systems employers operate in New Zealand?

Opportunities arise with the New Zealand Defence Force, Defence Technology Agency, defence industry suppliers and engineering firms serving land systems and vehicles. Many roles require citizenship or security clearance. A private online qualification may support an application, but it does not guarantee a position, and clearance and experience usually weigh more heavily.

Is it possible to study from New Zealand without a visa, and can my employer pay?

Yes, programmes are 100% online and taught in English, so no student visa is needed to study from home. Each includes an assigned technical tutor and a final project. Fees are payable in instalments, and employer sponsorship is possible. Specialist courses take 300 hours, diplomas 800 and master's 1,900, so plan your workload carefully.

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Learning outcomes

What you will be able to do

01

Understand the self-organisation principles of swarm systems.

02

Design stable consensus and formation control algorithms.

03

Solve distributed task allocation and coverage problems.

04

Implement cooperative perception and localisation.

05

Assess resilient communication architectures for swarms.

06

Apply multi-agent reinforcement learning with verification criteria.

07

Analyse the ethical and legal framework for autonomy in defence.

Profile

Who it's for

  • Multi-agent robotics engineers designing cooperative drone and robot teams.
  • Autonomous systems researchers working on swarm behaviour.
  • Distributed algorithms specialists in consensus, allocation and optimisation.
  • UAS integration engineers deploying multi-drone systems.
  • AI ethics analysts assessing autonomy in defence.
  • ROS developers building distributed robotic software.
  • Engineering or science graduates seeking to enter the defence sector.
Standards & tools

What you'll work with

  • DoD Directive 3000.09 (autonomy in weapon systems)
  • NATO Principles of Responsible Use of AI (lawfulness, accountability, explainability, reliability, governability and bias mitigation)
  • STANAG 4586 (UAV control system interoperability)
  • Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
  • IEEE 7000-2021 (addressing ethical concerns during system design)
  • ISO/IEC 23894:2023 (AI risk management)
  • ROS 2
  • Gazebo
  • ARGoS
  • PettingZoo
  • ns-3
Syllabus

8 modules

  1. 1.1Self-organisation and emergent behaviour
  2. 1.2Biological models: ants, bees and flocks
  3. 1.3Reynolds rules: cohesion, alignment and separation
  4. 1.4Stigmergy and indirect communication
  5. 1.5Local control versus global behaviour
  6. 1.6Taxonomy of collective tasks
  7. 1.7Scalability, robustness and flexibility
  8. 1.8Collective performance metrics
  9. 1.9Reference projects in swarm robotics
  10. 1.10Practical exercise: flocking simulation in Python
  1. 2.1Graph theory and the Laplacian matrix
  2. 2.2Distributed average consensus
  3. 2.3Displacement- and distance-based formation
  4. 2.4Leader following and virtual structure
  5. 2.5Artificial potential fields
  6. 2.6Lyapunov stability in multi-agent systems
  7. 2.7Algebraic connectivity
  8. 2.8Delays and switching topologies
  9. 2.9Collision avoidance with ORCA
  10. 2.10Lab: formation control with ROS 2
  1. 3.1Particle swarm optimisation
  2. 3.2Ant colony optimisation
  3. 3.3Auctions and the CBBA algorithm
  4. 3.4Hungarian assignment and distributed variants
  5. 3.5Area coverage with Voronoi partitions
  6. 3.6Collaborative exploration and mapping
  7. 3.7Multi-agent path planning
  8. 3.8Energy management and recharging
  9. 3.9Reallocation after agent failures
  10. 3.10Case study: allocating search and rescue zones
  1. 4.1Multi-robot SLAM
  2. 4.2Distributed sensor fusion
  3. 4.3Distributed Kalman filter
  4. 4.4Relative localisation with UWB
  5. 4.5Cooperative vision
  6. 4.6Navigation in GNSS-denied environments
  7. 4.7Merging partial maps
  8. 4.8Cooperative object detection
  9. 4.9Cross-covariance and consistency
  10. 4.10Practical exercise: SLAM with two robots in Gazebo
  1. 5.1MANET and FANET networks
  2. 5.2Ad hoc routing protocols
  3. 5.3DDS middleware in ROS 2
  4. 5.4Bandwidth and link degradation
  5. 5.5Authentication and encryption between nodes
  6. 5.6Resilience to loss of connectivity
  7. 5.7Distributed time synchronisation
  8. 5.8Interoperability under STANAG 4586
  9. 5.9Network simulation with ns-3
  10. 5.10Workshop: simulated drone mesh network
  1. 6.1Fundamentals of MARL
  2. 6.2Centralised training and decentralised execution
  3. 6.3Designing collective rewards
  4. 6.4Simulation-to-reality transfer
  5. 6.5Imitation learning
  6. 6.6Explainability of learned policies
  7. 6.7Robustness to disturbances
  8. 6.8PettingZoo environments
  9. 6.9Verification of learned behaviours
  10. 6.10Lab: MAPPO training on a coverage task
  1. 7.1Meaningful human control
  2. 7.2DoD Directive 3000.09 on autonomy
  3. 7.3NATO principles of responsible use of AI
  4. 7.4CCW debates on autonomous systems
  5. 7.5International humanitarian law and autonomy
  6. 7.6EU AI Act and exclusions
  7. 7.7Human–swarm interfaces
  8. 7.8Verification, validation and certification
  9. 7.9Dual uses: emergencies and logistics
  10. 7.10Case study: ethical assessment of a search swarm
  1. 8.1Definition of the non-lethal mission
  2. 8.2Functional and safety requirements
  3. 8.3Distributed control architecture
  4. 8.4Selected allocation algorithms
  5. 8.5Swarm simulation
  6. 8.6Robustness and scalability analysis
  7. 8.7Ethical and legal assessment
  8. 8.8Experimental validation plan
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

The programme is delivered 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 each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, Gazebo, ARGoS, PettingZoo and ns-3, in free, academic or evaluation versions. Testing is studied through real procedures for swarm simulation in virtual environments, indoor formation trials with micro-drones and cooperative search and rescue exercises. Assessment is continuous by module, plus the final project.

Standards covered: DoD Directive 3000.09, NATO Principles of Responsible Use of AI, STANAG 4586, Regulation (EU) 2024/1689, IEEE 7000-2021, ISO/IEC 23894:2023.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of DoD Directive 3000.09, the NATO Principles of Responsible Use of AI and STANAG 4586 applied to real cases, not as a theoretical annex.
Industry software:ROS 2, Gazebo, ARGoS, PettingZoo and ns-3, in free, academic or evaluation versions.
Final project:A swarm robotics project for a non-lethal mission covering requirements, distributed control architecture, allocation algorithms, swarm simulation, robustness analysis, ethical and legal assessment and experimental validation plan, 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.

Mission and requirements
Define a non-lethal mission and its functional and safety requirements.
Control architecture
Design the distributed control architecture and select the allocation algorithms.
Swarm simulation
Simulate the swarm in ROS 2 and Gazebo and analyse robustness and scalability.
Ethical and legal assessment
Assess the system against DoD Directive 3000.09, NATO AI principles and the EU AI Act.
Final project
Develop a complete swarm robotics project with an experimental validation plan.
Career opportunities

Where this takes you

Multi-agent robotics engineerAutonomous systems researcherDistributed algorithms specialistUAS integration engineerAI ethics analystROS developer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer able to run the programme's 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.

FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 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, ARGoS, PettingZoo and ns-3, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, 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.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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