Defense Drone Swarm Coordination

Multi-Agent Path Planning with Collision Avoidance

Coordinate autonomous defense drones to intercept alien scout pods while avoiding mid-air collisions. This page demonstrates Reynolds flocking rules — a decentralized behavioral model, not a centralized optimization solver.

The Scenario

Swarm Intercept

A formation of 15 autonomous defense drones must intercept 4 alien scout pods entering Earth's airspace. Each drone must reach its assigned target while avoiding collisions with other drones. The optimal multi-agent trajectory problem is computationally intractable for even moderate swarm sizes — instead, we use Reynolds flocking rules (1987), where each drone follows simple local behavioral rules that produce emergent coordinated motion.

Defense Domain	OR Element	Symbol
Defense drone	Agent with position, velocity	pᵢ(t), vᵢ(t)
Alien scout pod	Goal position	gᵢ
Collision zone	Safety distance	d_safe = 25 px
Steering force	Control input	uᵢ(t)
Sensor range	Neighbourhood radius	r_sense

// What the demo implements — Reynolds Flocking (1987): uᵢ(t) = w₁·separation(i) + w₂·alignment(i) + w₃·cohesion(i) + w₄·goal_seek(i) separation(i) = Σˇ∈Nᵢ (pᵢ - pˇ) / ||pᵢ - pˇ||² // repel nearby alignment(i) = avg(vˇ for j ∈ Nᵢ) // match velocity cohesion(i) = avg(pˇ for j ∈ Nᵢ) - pᵢ // steer toward center goal_seek(i) = (gᵢ - pᵢ) / ||gᵢ - pᵢ|| // head toward target Nᵢ = {j : ||pᵢ - pˇ|| ≤ r_sense} // local neighbourhood

★☆☆ Educational Demo

This is a behavioural simulation, not an optimization solver. The optimal multi-agent trajectory problem (minimizing total path length subject to collision avoidance constraints for n agents) is computationally intractable for n > ~10. Reynolds flocking provides a practical decentralized heuristic that produces emergent coordination without solving the full optimal control problem.

Interactive Simulation

Swarm Demo

Reynolds Flocking Simulation

★☆☆ Educational Demo

Separation w₁ 2.0 Alignment w₂ 0.3 Cohesion w₃ 0.1 Goal Seek w₄ 1.0 Max Speed 3.0

References

Published Reynolds, C.W. (1987). “Flocks, herds and schools: A distributed behavioral model.” SIGGRAPH Computer Graphics, 21(4), 25–34. — Original flocking model with separation, alignment, cohesion.

Published Olfati-Saber, R. (2006). “Flocking for multi-agent dynamic systems: algorithms and theory.” IEEE Trans. Automatic Control, 51(3), 401–420. — Formal stability analysis of flocking algorithms.

Published Van Den Berg, J., Lin, M., & Manocha, D. (2008). “Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation.” IEEE ICRA. — Collision avoidance via velocity obstacle computation.

Preparing for First Contact

If the aliens arrive, we suspect you will not be visiting a GitHub Pages site. We do recommend the Hungarian algorithm. It works on any planet.

All Defense Applications GitHub

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Educational Fiction Disclaimer

This is a fictional educational scenario.

All “alien invasion” content exists purely to teach OR concepts
All data and parameters are entirely fictional
No actual military applications are intended or endorsed
The author advocates for peace and opposes militarization