Will biomimetic robots be able to change a hivemind to guide honeybees' ecosystem services?

We study whether or not a group of biomimetic waggle dancing robots is able to significantly influence the swarm-intelligent decision making of a honeybee colony, e.g. to avoid foraging at dangerous food patches using a mathematical model. Our model was successfully validated against data from two empirical experiments: one examined the selection of foraging targets and the other cross inhibition between foraging targets. We found that such biomimetic robots have a significant effect on a honeybee colony's foraging decision. This effect correlates with the number of applied robots up to several dozens of robots and then saturates quickly with higher robot numbers. These robots can reallocate the bees' pollination service in a directed way towards desired locations or boost it at specific locations, without having a significant negative effect on the colony's nectar economy. Additionally, we found that such robots may be able to lower the influx of toxic substances from potentially harmful foraging sites by guiding the bees to alternative places. These effects also depend on the saturation level of the colony's nectar stores. The more nectar is already stored in the colony, the easier the bees are guided by the robots to alternative foraging targets. Our study shows that biomimetic and socially immersive biomimetic robots are a relevant future research target in order to support (a) the bees by guiding them to safe (pesticide free) places, (b) the ecosystem via boosted and directed pollination services and (c) human society by supporting agricultural crop pollination, thus increasing our food security this way.

Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay.

We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential.