Control

Updated from an original post on 14 Apr 2026. I recently integrated a Pull Request from OldCrow on our Web Shepherd web application. They have made substantial improvements to scalability, replacing the original O(n²)-complex interaction loop with spatial indexing and other techniques. This extends the practical range from a few hundred agents to a few thousand agents while maintaining smooth real-time performance in most Web browsers. To learn more: The full interactive demo has been updated A slimmer version for website integration is available, which you may have noticed on the homepage Code and Documentation has been updated on GitHub References Van Havermaet, S., Simoens, P., Landgraf, T., & Khaluf, Y. (2023). Steering herds away from dangers in dynamic environments. Royal Society Open Science, 10(5), 230015. ...

I recently had the pleasure of presenting our work at the 2026 Ingenuity Labs Research Institute Multi-robots Workshop. We explored a central theme across our research: when integrated within a control-theoretic framework, machine learning can enhance the performance of physical systems while preserving formal guarantees of stability and robustness. Specifically, we discussed how the swarm embeddings proposed in our recent work can be reframed as equilibrium manifolds to ensure swarms can maintain coherence and stability while learning to adapt to their environment. ...

A fully open architecture implementation of modern multi-agent coordination techniques. All agents make individual decisions based on local information only. There is no global plan. This is an open project I use to explore new ideas, validate theoretical results, and produce data for my academic research. ...