RESUMEN
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.
RESUMEN
Self-organized collective behavior has been analyzed in diverse types of gregarious animals. Such collective intelligence emerges from the synergy between individuals, which behave at their own time and spatial scales and without global rules. Recently, robots have been developed to collaborate with animal groups in the pursuit of better understanding their decision-making processes. These biohybrid systems make cooperative relationships between artificial systems and animals possible, which can yield new capabilities in the resulting mixed group. However, robots are currently tailor-made to successfully engage with one animal species at a time. This limits the possibilities of introducing distinct species-dependent perceptual capabilities and types of behaviors in the same system. Here, we show that robots socially integrated into animal groups of honeybees and zebrafish, each one located in a different city, allowing these two species to interact. This interspecific information transfer is demonstrated by collective decisions that emerge between the two autonomous robotic systems and the two animal groups. The robots enable this biohybrid system to function at any distance and operates in water and air with multiple sensorimotor properties across species barriers and ecosystems. These results demonstrate the feasibility of generating and controlling behavioral patterns in biohybrid groups of multiple species. Such interspecies connections between diverse robotic systems and animal species may open the door for new forms of artificial collective intelligence, where the unrivaled perceptual capabilities of the animals and their brains can be used to enhance autonomous decision-making, which could find applications in selective "rewiring" of ecosystems.
RESUMEN
Endothermic heat production is a crucial evolutionary adaptation that is, amongst others, responsible for the great success of honeybees. Endothermy ensures the survival of the colonies in harsh environments and is involved in the maintenance of the brood nest temperature, which is fundamental for the breeding and further development of healthy individuals and thus the foraging and reproduction success of this species. Freshly emerged honeybees are not yet able to produce heat endothermically and thus developed behavioural patterns that result in the location of these young bees within the warm brood nest where they further develop and perform tasks for the colony. Previous studies showed that groups of young ectothermic honeybees exposed to a temperature gradient collectively aggregate at the optimal place with their preferred temperature of 36 °C but most single bees do not locate themselves at the optimum. In this work we further investigate the behavioural patterns that lead to this collective thermotaxis. We tested single and groups of young bees concerning their ability to discriminate a local from a global temperature optimum and, for groups of bees, analysed the speed of the decision making process as well as density dependent effects by varying group sizes. We found that the majority of tested single bees do not locate themselves at the optimum whereas sufficiently large groups of bees are able to collectively discriminate a suboptimal temperature spot and aggregate at 36 °C. Larger groups decide faster than smaller ones, but in larger groups a higher percentage of bees may switch to the sub-optimum due to crowding effects. We show that the collective thermotaxis is a simple but well evolved, scalable and robust social behaviour that enables the collective of bees to perform complex tasks despite the limited abilities of each individual.
