a flock of birds flying through a blue sky — Photo by Danielle-Claude Bélanger on Unsplash

I am delighted to share a significant milestone in my research journey: the acceptance of our latest paper, “A Multi-Objective Scheme for Collision Avoidance, Swarm Cohesion, and Target Tracking for Smart UAVs,” for publication in the Journal of Information and Intelligence.

This work represents several years of development, collaboration, reflection, refinement — and most importantly, a deep fascination with how artificial intelligence can push intelligent aerial systems into entirely new territory.

In this blog post, I want to take the opportunity to describe not just the technical details, but the intellectual narrative behind the research, the people and organisations who made it possible, and how this work fits into a much larger continuum of ideas.

🚁 Why UAV Swarm Intelligence Matters

Unmanned Aerial Vehicles are no longer just flying sensors or remote-controlled devices. Increasingly, they are becoming autonomously intelligent systems capable of:

sensing
decision-making
coordination
adaptation
collective behaviour

When multiple UAVs work together cooperatively, they can accomplish feats that a single drone never could:

searching complex environments efficiently
forming dynamic formations
collectively tracking moving targets
supporting search-and-rescue missions
surveying hazardous or inaccessible regions

But making such behaviours stable, safe, and reliable is enormously challenging — especially when seven UAVs are learning simultaneously, as in our study.

Swarm intelligence is delicate. If drones fly too close, they risk collision. If they spread too far apart, the swarm loses coherence. If they track the target too aggressively, they destabilise; if too passively, they fall behind.

Our goal was to build a learning-based testbed in which UAVs discover behaviours that naturally balance all three objectives:

1. Collision avoidance
2. Swarm cohesion
3. Target tracking

This required innovation across simulation engineering, artificial intelligence, control theory, and mathematical modelling.

🧠 Reinforcement Learning at the Core

The heart of our system is Reinforcement Learning (RL) — a type of AI inspired by how organisms learn through trial and error. Instead of being explicitly programmed, UAVs:

observe their environment
choose actions
receive rewards or penalties
update their behaviour
gradually become more skilled

We designed a dual-model structure:

A2C

Controls the target UAV, which performs random but physically realistic manoeuvres.

A3C

Controls seven tracking UAVs, each governed by a separate asynchronous worker, enabling parallel learning and higher exploration diversity.

To make learning more effective, we included an Intrinsic Curiosity Module (ICM), which allows drones to reward themselves for exploring unfamiliar states. This is essential in environments where external rewards are sparse or delayed — a frequent challenge in multi-agent flight scenarios.

📐 The Ellipsoid: A New Way to Think About Space and Safety

One of the key innovations in this research is our use of 3D ellipsoids to define “safety spaces” around each UAV.

A simple sphere could work, but real aircraft dynamics aren’t symmetric:

they extend more along particular axes
orientation matters
distance alone is not enough

By using ellipsoids aligned with each UAV’s orientation, we created a geometrically meaningful safety envelope. This allowed us to mathematically express:

how close two UAVs are
whether that distance is safe
whether they are aligned with each other
how far they should remain from the target for optimal tracking

To build intelligence around this, we wrapped a Gaussian reward function around the ellipsoidal boundary.
This means:

maximum reward = exactly on the boundary
penalties = too close or too far
smooth gradient = stable learning

This mathematical framework is one of the strongest contributions of the paper — and integral to the elegant behaviour shown in the trajectories.

🧪 Real-Time Simulation with FlightGear and JSBSim

Our testbed is fully integrated with:

FlightGear for 3D simulation
JSBSim for realistic flight dynamics
UDP networking for high-speed communication

All seven UAVs plus the target operate simultaneously in real time. This is not a simplified physics environment — it is grounded in real flight dynamics, giving the results credibility and transfer potential.

🌱 Intellectual Roots: The NUAV Testbed and the Namal Education Foundation

Every research project stands on the contributions of earlier work.
In our case, one of the most important inspirations was the NUAV Testbed, whose development was originally funded by the Namal Education Foundation.

The NUAV Testbed was one of the early attempts to create:

an accessible UAV simulation environment
a modular architecture
a cost-effective flight testing system
infrastructure for experimentation in autonomy

Its philosophy of openness, affordability, and rigorous experimentation helped inspire key architectural decisions in our current system. While our work moves significantly beyond the original design — adding multi-agent RL, curiosity-driven learning, and ellipsoidal safety geometry — the intellectual DNA of NUAV remains present.

It is important to recognise this evolution. Research is a continuum, and we are proud to build upon a foundation that was shaped years earlier through the support of the Namal Education Foundation.

🧩 A Special Acknowledgment: Dr. Junaid Akhtar

A project of this scale requires not only technical effort but also the conceptual clarity needed to lay out a compelling research proposal.
For that, I want to express my deep gratitude to Dr. Junaid Akhtar.

Dr. Akhtar holds a PhD in non-Darwinian schemes for evolutionary computation — a highly specialised and intellectually demanding field. His expertise in alternative evolutionary paradigms, theoretical modelling, and computational intelligence is remarkable.

During the proposal development stage, his insights:

sharpened the conceptual direction,
strengthened the problem formulation,
deepened the evolutionary computation perspective,
and helped shape a proposal that was both technically ambitious and academically solid.

His support was instrumental, and I am grateful for his contributions.
It is a privilege to receive guidance from a scientist of his calibre.

🤝 Celebrating Collaboration

No research endeavour is done alone. I am fortunate to have worked with:

Jawad Mahmood
Dr. John Loane
Professor Fergal McCaffery

Their expertise, commitment, and collaborative energy powered every stage of this project — from initial conceptualisation to simulation to manuscript preparation.

I am honoured to share authorship with them.

🇮🇪 Funding That Made This Possible

This research was funded by the
Technological University Transformation Fund (TUTF)
of the
Higher Education Authority (HEA) of Ireland.

Their support for innovative, forward-looking research in AI and autonomy has created a thriving environment for ambitious projects such as this one. We are sincerely grateful for this backing.

🚀 Looking Toward the Future

The development of this testbed opens exciting new possibilities:

deploying UAV swarms in real-world experiments
integrating explainable AI for safer autonomous behaviour
studying adversarial or cooperative swarm strategies
expanding multi-objective learning frameworks
applying swarm AI to environmental monitoring and disaster response

This is only the beginning.

The future of intelligent UAV swarms — dynamic, adaptive, curiosity-driven, and cooperative — holds immense promise. I am excited to continue pushing the boundaries of what is possible.

Thank you for reading, and thank you to everyone who supported this journey.
If you have questions, ideas, or interest in collaboration, I would be delighted to connect.