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A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees

Stefanec, Martin; Hofstadler, Daniel N.; Krajník, Tomáš; Turgut, Ali Emre; Alemdar, Hande; Lennox, Barry; Şahin, Erol; Arvin, Farshad; Schmickl, Thomas


Martin Stefanec

Daniel N. Hofstadler

Tomáš Krajník

Ali Emre Turgut

Hande Alemdar

Barry Lennox

Erol Şahin

Thomas Schmickl


Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through “Ecosystem Hacking”. We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen’s court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.


Stefanec, M., Hofstadler, D. N., Krajník, T., Turgut, A. E., Alemdar, H., Lennox, B., …Schmickl, T. (2022). A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees. Frontiers in Robotics and AI, 9, Article 791921.

Journal Article Type Article
Acceptance Date Mar 2, 2022
Online Publication Date Apr 28, 2022
Publication Date 2022
Deposit Date May 27, 2022
Journal Frontiers in Robotics and AI
Electronic ISSN 2296-9144
Publisher Frontiers Media
Volume 9
Article Number 791921
Public URL