No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Governing the swarm: Controlling a bio-hybrid society of bees & robots with computational feedback loops
... An alternative approach, central to robotics investigating social interactions, is to present stimuli externally, that is, in a non-invasive manner. Robotics have been developed to interact with a variety of species, including vertebrates such as ducks [26], chickens [27], frogs [28], electric fish [29], zebrafish [30], [31], and rummy-nose tetra fish [32]; and invertebrates such as cockroaches [12], ants [33], stag beetles [34], crickets [35], flies [36], [37], and honeybees [22], [38]- [43], underscoring the transformative potential of robotics in the domain [44]. Examining the results obtained with robotics that interact with honeybees more closely, the works have covered several important social behaviors using a variety of cues. ...
... Examining the results obtained with robotics that interact with honeybees more closely, the works have covered several important social behaviors using a variety of cues. These include: investigating the waggle dance by mimicking its motion and incorporating additional cues such as wing flapping [22], [41]; information exchange between different species [42] to examine the possibility of coordinating collective decisions; honeybees' responses to thermal cues, in small groups of young worker bees [38], [39], and within intact colonies during the winter [40]; and the potential for aggregating small groups of bees with vibrational cues, generated by a distributed set of static robots [39], [43]. ...
... Despite the growing interest in using robotics to investigate biological systems, only a few studies have successfully integrated robotic devices into honeybee groups (e.g., [22], [38], [41], [42], [106]). The robotic device in the present paper offers two relevant advances. ...
Social insects, such as ants, termites, and honeybees, have evolved sophisticated societies where collaboration and division of labor enhance survival of the whole colony, and are thus considered “superorganisms”. Historically, studying behaviors involving large groups under natural conditions posed significant challenges, often leading to experiments with a limited number of organisms under artificial laboratory conditions that incompletely reflected the animals’ natural habitat. A promising approach to exploring animal behaviors, beyond observation, is using robotics that produce stimuli to interact with the animals. However, their application has predominantly been constrained to small groups in laboratory conditions. Here we present the design choices and development of a biocompatible robotic system intended to integrate with complete honeybee colonies in the field, enabling exploration of their collective thermoregulatory behaviors via arrays of thermal sensors and actuators. We tested the system’s ability to capture the spatiotemporal signatures of two key collective behaviors. A 121-day observation revealed thermoregulation activity of the broodnest area during the foraging season, followed by clustering behavior during winter. Then we demonstrated the system’s ability to influence the colony by guiding a cluster of bees along an unnatural trajectory, via localized thermal stimuli emitted by two robotic frames. These results showcase a system with the capability to experimentally modulate honeybee colonies from within, as well as to unobtrusively observe their dynamics over extended periods. Such biohybrid systems uniting complete societies of thousands of animals and interactive robots can be used to confirm or challenge the existing understanding of complex animal collectives.
... We call such sensoractuator nodes combined actuator sensor units (CASUs), as they are described in Schmickl et al. (2013) and Griparić et al. (2017). Experiments with static arrays of CASUs were performed by modulating honeybee aggregations (e.g., Stefanec et al., 2017a;Mariano et al., 2018) and by guiding plant growth (Wahby et al., 2018). In such a static array, the agents themselves cannot move, but they can emit stimulus patterns that show spatiotemporal dynamics, sometimes produced by nearest-neighbour interactions of adjacent robots in the topology, similar to how cells do in cellular automata (Wolfram, 1983). ...
... This experiment used a pair of CASUs enclosed by a stadiumshaped arena. In contrast to experiment B1, which showed how bees interact without active robot influence, here, the robots were programmed in a way that they create an additional feedback loop in the system that can enhance or suppress the natural symmetry-breaking capabilities of the bees (Stefanec et al., 2017a). To achieve this, each CASU used its local IR sensors to estimate the local bee density around it and regulated its local temperature in a positive or negative correlation with this estimate (detailed below). ...
... These dynamics are replicated in the model results (lower sub-panel). (C) Honeybee group decisions in modelling a robot-mediated thermal environment with closed-loop control and how this agrees with empirical data (empirical experiments, reported in Stefanec et al., 2017a), and how the modelling results agree with empirical trends. N = 14 independent repetitions in each setting. ...
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.
... Advancements in computer hardware and algorithms have facilitated the development of smaller and more agile robotic systems that can be operated with improved perception systems and increasingly sophisticated motion models. Over the years, the combination of these advances, along with a continued interest in deciphering the rules that govern collective behavior, has led to the design of a plethora of biohybrid systems, spanning from groups of fish [2], [5], [7], [18], [33], [35], [42], [45], [48]- [50], [52], [59], bees [1], [23], [37], [58], insects [24], [46], rats [55], and birds [19], [21], [22], [28], [56]. While these systems primarily serve as a means to examine animal behavior, they also offer a glimpse into potential strategies for preserving ecosystems, thereby contributing to environmental conservation efforts [26], [53]. ...
... ), decision-making processes (e.g., utilizing computational behavioral models), and targeted communication chan-nels specific to the species under study. For example, bees may respond to air currents or hive temperature fluctuations [1], [10], [23], [58], while fish can be influenced by lures or visual stimuli [5], [7], [14], [18], [30], [33], [35], [36], [40], [42], [45], [48], [50], [59]. In this work, we focus on biohybrid systems designed to accommodate small animals, with an emphasis on fishrobot interactions. ...
The collective behavior of animals has been traditionally studied through observation, quantitative models of behavior, and devices of low intelligence. Nowadays, the advancements in the field of robotics allow for closed-loop experiments that occur in real-time, and for artificial agents that intelligently and autonomously blend into hybrid groups of animals and robots (i.e., biohybrid groups). Such systems provide scientists with the opportunity to study the social interactions that govern collective behavior from within the group of animals, either by mimicking them or by introducing precise stimuli to elicit and study their response. In this work, we introduce an open source Biohybrid Observation and Interaction (BOBI) platform for small animals, which consists of 3 main components: 1) an experimental setup; 2) a wheeled mobile robot called LureBot; 3) their software. We demonstrate the software and hardware design aspects that make BOBI stand out compared to our previous system and similarly purposed systems. Finally, we demonstrate our methodologies and the new robot’s agility, by conducting a set of preliminary experiments with rummy-nose tetra (Hemigrammus rhodostomus) fish, where we study: 1) the response of one fish swimming with a biomimetic and a non-biomimetic lure; 2) the robot behavior when the robot interacts biomimetically and in closed loop with the wall alone, with 1 fish, and with 4 fish. The experiments show that fish strongly prefer biomimetic lures, and that the robot is consistently successful in engaging in social interactions with the fish.
... Moreover, honeybees respond to low temperatures by endothermic heat generation (44), especially within the winter clustering behavior (45), when a colony forms a dynamic self-regulating aggregate of thousands of bees that behaves like one single larger organism, affording survival in cold climates. Recognizing the bees' sensitivity to temperature, prior research developed robotics that interacted with small groups of young honeybees, successfully modulating their behaviors with localized thermal stimuli under laboratory conditions (46)(47)(48). Therefore, a thermal pathway offers a promising candidate for a robotic platform to interact with entire colonies. Our goal is to leverage robotic capabilities in scientific studies of honeybee collective dynamics, specifically by means of robotic interactions that can provoke the animals' responses through modulation of localized thermal fields inside the hive. ...
Robotic technologies have shown the capability to interact with living organisms and even to form integrated mixed societies composed of living and artificial agents. Biocompatible robots, incorporating sensing and actuation capable of generating and responding to relevant stimuli, can be a tool to study collective behaviors previously unattainable with traditional techniques. To investigate collective behaviors of the western honeybee (Apis mellifera), we designed a robotic system capable of observing and modulating the bee cluster using an array of thermal sensors and actuators. We initially integrated the system into a beehive populated with about 4000 bees for several months. The robotic system was able to observe the colony by continuously collecting spatiotemporal thermal profiles of the winter cluster. Furthermore, we found that our robotic device reliably modulated the superorganism's response to dynamic thermal stimulation, influencing its spatiotemporal reorganization. In addition, after identifying the thermal collapse of a colony, we used the robotic system in a "life-support" mode via its thermal actuators. Ultimately, we demonstrated a robotic device capable of autonomous closed-loop interaction with a cluster comprising thousands of individual bees. Such biohybrid societies open the door to investigation of collective behaviors that necessitate observing and interacting with the animals within a complete social context, as well as for potential applications in augmenting the survivability of these pollinators crucial to our ecosystems and our food supply.
... This approach has been widely investigated by several researchers [4][5][6][7] due to the parallelism and multiobjective [8] that can occur in this system in relation to the traditional robotics usage [9], where a single robot can perform tasks sequentially [10,11]. The first swarm robotics approaches emerged in the artificial intelligence field, from the biological bees study [12], ants [4] and other populations where swarming behavior occurred [13,14]. Swarm robotics is inspired by the emerging behavior observed in social insects [15] or even pedestrians interactions [16], called swarm intelligence, but is not limited to this. ...
Swarm robotics is an area of research that has attracted several researchers. This field is part of the collective robotics approach that is inspired by the self-organized behaviors of social animals. From interactions between agents, guided by simple rules, it is possible to design emerging collective behaviors capable of performing complex tasks in an organized manner, for the coordination and control of a large number of robots. In this article, we proposed a new model that combines different techniques of natural and evolutionary computing: we mainly focus on ideas and concepts about cellular automata, social pedestrians behavior in evacuation, genetic algorithms, inverted pheromone from ant colonies and Tabu search, as hybrid search mechanism. The objective of this new model is to provide an advance of surrogate techniques for swarm robotics as a field of science and engineering and that may be relevant to deal with the robotic surveillance task. The new model is called Genetic Shared Tabu Inverted Ant Cellular Automata, or shortly GSTIACA. Initially, it was made a meta-optimization of the surrogate parameters through a genetic algorithm. Then, we apply these new parameters for hundreds steps in a robot control and navigation algorithm based on cellular automata techniques in different environments types. The system global search takes place at different times, one of which is based on the spread of pheromone in the environment and the other which is based on the memory sharing based on Tabu search. The novelty of this work is precisely the Tabu search application as a local and used as a shared global search algorithm. Besides that, we developed a new algorithm for swarm robotics that integrates different artificial intelligence techniques and natural computing not yet used all together in precursor works. In addition, we reduced the cost of processing the pheromone decline calculation using an asynchronous cellular-automaton. Later, we contrasted the new model in different situations, and saw that the new algorithm proposed here is better than its precursors. Finally, we did a test using the e-Puck architecture within the Webots simulation environment to prove that the mathematical model proposed herein is capable of being applied in the real world application.
... This approach has been widely investigated by several researchers [4,5,6,7] due to the parallelism and multi-objective [8] that can occur in this system in relation to the traditional robotics usage [9], where a single robot can perform tasks sequentially [10,11]. The first swarm robotics approaches emerged in the artificial intelligence field, from the biological bees study [12], ants [4] and other populations where swarming behavior occurred [13,14]. Swarm robotics is inspired by the emerging behavior observed in social insects [15] or even pedestrians interactions [16], called swarm intelligence, but is not limited to this. ...
Swarm robotics is an area of research that has attracted several researchers. This field is part of the collective robotics approach that is inspired by the self-organized behaviors of social animals. In this article, we proposed a new model that combines different techniques of natural and evolutionary computing: we mainly focus on ideas and concepts about cellular automata, social pedestrians behavior in evacuation, genetic algorithms, inverted pheromone from ant colonies and Tabu search, as hybrid search mechanism.The objective of this new model is to provide an advance of surrogate techniques for swarm robotics as a field of science and engineering and that may be relevant to deal with the robotic surveillance task. The new model is called Genetic Shared Tabu Inverted Ant Cellular Automata, or shortly GSTIACA. Initially, it was made a meta-optimization of the surrogate parameters through a genetic algorithm. Then, we apply these new parameters for hundreds steps in a robot control and navigation algorithm based on cellular automata techniques in different environments types. The novelty of this work is precisely the Tabu search application as a local and used as a shared global search algorithm. We contrasted the new model in different situations, and saw that the new algorithm proposed here is better than its precursors. Finally, we did a test using the e-Puck architecture within the Webots simulation environment to prove that the mathematical model proposed herein is capable of being applied in the real world application.
... This concept requires the development of robotic technology that can interact with natural organisms. Going beyond mere robot-organism interactions, which were achieved in isolated lab experiments several times in the past Landgraf et al. 2016;Stefanec et al. 2017;Bonnet et al. 2018;Heinrich et al. 2019), our paradigm presented here aims at influencing organisms in their natural habitat, with the goal of either supporting their survival, or even replacing them in case they went extinct or declined significantly in population numbers. In this case, a robotic surrogate can take over the stabilizing role of this species in the ecosystem, which is especially crucial if the decimated or extinct species is a keystone species. ...
Here we present the novel concept of a method called Ecosystem Hacking, in which the stability of decaying ecosystems is aimed to be supported by the introduction of biomimetic robots that interact with their natural counterparts. We briefly discuss previous research projects that established such robot-organism interactions with plants or animals under laboratory conditions and then describe the concepts of Ecosystem Hacking by discussing the objectives of the research project RoboRoyale, which aims to apply these technologies in honeybee colonies that forage “in the wild”. This will be achieved in a minimally invasive way, by just affecting the honeybee queen and her few courtyard bees. Specific technology has to be designed to interact with the living organism under natural conditions, thus the behaviour of the natural organisms has to be first observed, then classified and then mimicked, in order for such novel technological artifacts to be accepted by the living animals. Here, we describe novel approaches and concepts in a challenging research direction. Finally, we briefly discuss the potential, the required prerequisites, but also the potential dangers, of this new approach towards conservation biology.
... We expect that ideas and models from these scenarios and research fields could be fruitfully integrated in the design of novel SI systems. Integration of autonomous robots into existing animal societies [117][118][119] has been performed several times successfully with honeybees [120,121] , fish [122][123][124] , cockroaches [125] and cows [126] . This is an emerging field of science, as embedding robots in living animal societies will allow both sides to bring in their special capabilities and to merge them in a symbiotic way, in order to generate a novel bio-hybrid system, a social cyborg. ...
Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors. Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system haven’t really found their way from lab demonstrations to real-world applications, so far. This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios. In this paper, we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in autonomous driving and smart traffic, emergency response, environmental monitoring, electric energy grids, space missions, medical applications, and human networks. We do not aim to provide new solutions for the swarm intelligence or CPS community, but rather build a bridge between these two communities. This allows us to view the research problems of swarm intelligence from a broader perspective and motivate future research activities in modeling, design, validation/verification, and human-in-the-loop concepts.
... Landgraf et al. [11] showed that a mobile robot with realistic eye dummies and natural motion patterns significantly improved its acceptance level among a shoal of Guppies. Stefanec et al. [12] showed that static robots simulating the presence of individuals by controlling their temperatures could interact and attract young bees. Worm et al. [13] showed that a robot moving randomly can interact with the electric fish Mormyrus rume by displaying prerecorded electric organ discharges (EODs) in answer to the EODs made by the live fish. ...
The objective of this study is to integrate biomimetic robots into small groups of zebrafish and to modulate their collective behaviours. In this study, we explore different strategies that use biomimetic zebrafish behaviours. In past work, we have shown that robots biomimicking zebrafish can be socially integrated into zebrafish groups. We have also shown that a fish-like robot can modulate the rotation choice of zebrafish groups in a circular set-up. Here, we further study the modulation capabilities of such robots in a more complex set-up. To do this, we exploit zebrafish social behaviours we identified in previous studies. We first modulate collective departure by replicating the leadership mechanisms with the robot in a set-up composed of two rooms connected by a corridor. Then, we test different behavioural strategies to drive the fish groups towards a predefined target room. To drive the biohybrid groups towards a predefined choice, they have to adopt some specific fish-like behaviours. The first strategy is based on a single robot using the initiation behaviour. In this case, the robot keeps trying to initiate a group transition towards the target room. The second strategy is based on two robots, one initiating and one staying in the target room as a social attractant. The third strategy is based on a single robot behaving like a zebrafish but staying in the target room as a social attractant. The fourth strategy uses two robots behaving like zebrafish but staying in the target room. We conclude that robots can modulate zebrafish group behaviour by adopting strategies based on existing fish behaviours. Under these conditions, robots enable the testing of hypotheses about the behaviours of fish.
... Of course, in the discussion about assisted evolution we should not exclude alternative ideas from other disciplines. For example, this can include the extension of the genetic code through xenobiological applications (Hoshika et al., 2019) or even fusing organisms with machine components in order to enhance their survival, as for example demonstrated by enhanced performance of bee colonies after inserting small guidance-robots in their nest (Stefanec et al., 2017). But no matter how much the conceptual framework of assisted evolution is expanded, an essential question will be whether a targeted modification of wild organisms and populations in order to match ideas of astrobiological colonization is ethically justifiable (Filbee-Dexter and Smajdor, 2019). ...
In ecology and conservation biology, the concept of assisted evolution aims at the optimization of the resilience of organisms and populations to changing environmental conditions. What has hardly been considered so far is that this concept is also relevant for future astrobiological research, since in artificial extraterrestrial habitats (e.g., plants and insects in martian greenhouses) novel environmental conditions will also affect the survival and performance of organisms. The question therefore arises whether and how space-relevant organisms can be artificially adapted to the desired circumstances in advance. Based on several adaptation and acclimatization strategies in wild ecosystems of Earth, I discuss which methods can be considered for assisted evolution in the context of astrobiological research. This includes enhanced selective breeding, induction of epigenetic inheritance, and genetic engineering, as well as possible problems of these applications. This short overview article aims to stimulate an emerging discussion as to whether humans, which are already prominent drivers of Earth's evolution, should consider such interventions for future planetary colonization as well.
... Therefore, in the past years, robots and artificial lures allowed scientists to put these theoretical models to the test in real-life scenarios and with true feedback from the animals, in order to study their collective behavior. Thus, they have since been increasingly involved in inferring the rules of interaction among animals such as bees [19][20][21], fish [22][23][24][25][26][27], birds [28][29][30] and cockroaches [31]. Some of them [32][33][34] relied on the use of teleoperated devices that produce signals (e.g., visual, acoustic, electric) to attract or repel the animals, others rely on mobile robots that are not explicitly mimicking the animal under study (e.g., it could be a sheepdog among sheep [35]) and some relied on mimetic lures, that is, on lures that mimic the shape, size, and appearance or behavior [22,23,31,36,37]. ...
Many studies on collective animal behavior seek to identify the individual rules that underlie collective patterns. However, it was not until the recent advancements of micro-electronic and embedded systems that scientists were able to create mixed groups of sensor-rich robots and animals and study collective interactions from the within a bio-hybrid group. In recent work, scientists showed that a robot-controlled lure is capable of influencing the collective decisions of zebrafish Danio rerio shoals moving in a ring and a two-room setup. Here, we study a closely related topic, that is, the collective behavior patterns that emerge when different behavioral models are reproduced through the use of a robotic lure. We design a behavioral model that alternates between obeying and disobeying the collective motion decisions in order to become socially accepted by the shoal members. Subsequently, we compare it against two extreme cases: a reactive and an imposing decision model. For this, we use spatial, directional and information theoretic metrics to measure the degree of integration of the robotic agent. We show that our model leads to similar information flow as in freely roaming shoals of zebrafish and exhibits leadership skills more often than the open-loop models. Thus, in order for the robot to achieve higher degrees of integration in the zebrafish shoal, it must, like any other shoal member, be bidirectionally involved in the decision making process. These findings provide insight on the ability to form mixed societies of animals and robots and yield promising results on the degree to which a robot can influence the collective decision making.
... However, after 20 min, the two systems stabilized, and the two animal groups made a collective decision together (see movie S3). More broadly across the experiments, bees made strong decisions frequently in B → F, as we have observed in previous work (33); the ability to reach and maintain aggregations was disrupted by the influence of the fish in B ⇌ F and even more so in B ← F (see text S5). ...
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.
... BEECLUST [4] has been used in different research studies with real-and simulatedrobots. Follow up studies on BEECLUST focused on: i) derivation of aggregation model based on the systematic honeybee experiments [13], [14], [15], ii) modification of parameters of BEECLUST to improve the performance [16], iii) macroscopic modelling of the aggregation [17], [18], iv) fuzzy-based decisioning [19], v) heterogeneity in behaviours [20], and vi) recently a bio-hybrid society of robots and honeybees [21], [22]. ...
... A major milestone is the robotic cockroaches of Halloy et al. (2007), in which a group of robots were able to successfully integrate with a group of cockroaches, and, when programmed with unusual environmental preferences were able to steer the natural cockroaches into a different decision than they would take alone. Closed-loop coupling of animals and robots have been explored with various organisms such as ducks (Vaughan et al., 2000), fish (Swain et al., 2012;Bonnet et al., 2018), and honeybees (Landgraf et al., 2012;Griparic et al., 2017;Stefanec et al., 2017). ...
In this paper we report the first results of evolving bio-hybrid societies. Our goal is to have robots that are integrated in an animal society, and here we evolve robot controllers using animals as fitness providers, directly judging the success of integration.
In particular, we are using juvenile honeybees and robots that are able to produce vibration patterns.
Previous studies have shown that honeybees react to different vibration patterns, such as exhibiting freezing or stopping behaviours.
In this paper we investigate whether we are able to evolve a vibration pattern that stops bees in a small region.
Honeybees were placed in two containers with no communication between them: one with an active, vibrating robot, and a second with a passive robot.
Post-hoc evaluations of key evolved chromosomes generally confirm fitness values obtained during evolution.
We also tested the transferability of key chromosomes to a single container, in which bees are free to visit one vibrating and two dummy robots.
Encouragingly, most chromosomes are able to selectively stop bees, i.e., only in the vicinity of the vibrating robot, despite having been evolved under the more constrained setup.
These results speak to the value of an evolutionary approach for discovering how to interact with animals.
... Autonomous robots are capable to interact with animals and can serve as tools to study social dynamics [31]. This approach has already been used in studies to analyse the behaviour of ducks [41], drosophila [42], cockroaches [21], fish [10], [27], [28], [25], [24], [6], [2], bees [20], [29], [38] and birds [23], [14], [19]. ...
We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.
... Autonomous robots are capable to interact with animals and can serve as tools to study social dynamics [31]. This approach has already been used in studies to analyse the behaviour of ducks [41], drosophila [42], cockroaches [21], fish [10], [27], [28], [25], [24], [6], [2], bees [20], [29], [38] and birds [23], [14], [19]. ...
We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.
Robotics has gained significant attentions in recent years, thanks to dramatic developments in the field, and the impact that it can deliver on the supply of materials and services. In this chapter, an overview of robotic technologies is presented and discussed. Then, a diverse range of applications including the medical industry, space exploration, military, education, agriculture, oil and gas industry, textile, railcar industry, maintenance and repair, construction industry, environmental issues, security service, social assistance, travel industry, and human‐interactive applications are reviewed. The feature of interest is the impact that robotic technology can deliver as one of the major drivers of the Industry 4.0 revolution.
In this paper, we present a novel robotic system developed for researching collective social mechanisms in a biohybrid society of robots and honeybees. The potential for distributed coordination, as observed in nature in many different animal species, has caused an increased interest in collective behaviour research in recent years because of its applicability to a broad spectrum of technical systems requiring robust multi-agent control. One of the main problems is understanding the mechanisms driving the emergence of collective behaviour of social animals. With the aim of deepening the knowledge in this field, we have designed a multi-robot system capable of interacting with honeybees within an experimental arena. The final product, stationary autonomous robot units, designed by specificaly considering the physical, sensorimotor and behavioral characteristics of the honeybees (lat. Apis mallifera), are equipped with sensing, actuating, computation, and communication capabilities that enable the measurement of relevant environmental states, such as honeybee presence, and adequate response to the measurements by generating heat, vibration and airflow. The coordination among robots in the developed system is established using distributed controllers. The cooperation between the two different types of collective systems is realized by means of a consensus algorithm, enabling the honeybees and the robots to achieve a common objective. Presented results, obtained within ASSISIbf project, show successful cooperation indicating its potential for future applications.
Linhart‘s thermosolar hive was tested on its efficiency in suppressing the mite Varroa destructor Anderson & Trueman 2000 in honey bee colonies. It has been experimentally verified that thermotherapy is highly effective in suppressing Varroa destructor. When the temperature of the brood chamber is allowed to reach and is maintained at 40 - 47 °C (104 - 116,6 °F) over a period of 2.5 hours, mortality of the mites in the sealed brood is virtually absolute, whereas bee brood withstands this temperature unharmed. Since thermotherapy is carried out with an open entrance, it is advisable to repeat the heating treatment cycle in order to achieve a highly effective elimination of the mites throughout the entire bee colony. The second treatment should be conducted after the remaining mites, which were carried by adult bees not present in the hive during the initial thermotherapy, transferred back to the brood. This occurs about 10 - 12 days after the first treatment.
Guaranteeing high acaricide efficacy to control Varroa destructor is fundamental for colony survival. In this study, we verified the efficacy and impact of a commercial thymol-based veterinary product (Apiguard®) on colony honey bee populations when used alone or combined with the biotechnical method of caging honey bee queens to create an artificial brood interruption period in the colony. Apiguard® killed 76.1% of the mites while queen caging killed 40.6% of the mites. The combination of Apiguard® administration with queen caging killed 96.8% of the mites. Comparing bee numbers before and after treatment, Apiguard® treated colonies with caged queens had 48.7% fewer bees compared to before treatment, while Apiguard® alone reduced the number of adult bees by 13.6%. None of the treatments in the different groups resulted in elevated queen mortality.
Honey bees have played a major role in the history and development of humankind, in particular for nutrition and agriculture. The most important role of the western honey bee (Apis mellifera) is that of pollination. A large amount of crops consumed throughout the world today are pollinated by the activity of the honey bee. It is estimated that the total value of these crops stands at 155 billion euro annually. The goal of the work outlined in this paper was to use wireless sensor network technology to monitor a colony within the beehive with the aim of collecting image and audio data. These data allows the beekeeper to obtain a much more comprehensive view of the in-hive conditions, an indication of flight direction, as well as monitoring the hive outside of the traditional beekeeping times, i.e. during the night, poor weather, and winter months. This paper outlines the design of a fully autonomous beehive monitoring system which provided image and sound monitoring of the internal chambers of the hive, as well as a warning system for emergency events such as possible piping, dramatically increased hive activity, or physical damage to the hive. The final design included three wireless nodes: a digital infrared camera with processing capabilities for collecting imagery of the hive interior; an external thermal imaging camera node for monitoring the colony status and activity, and an accelerometer and a microphone connected to an off the shelf microcontroller node for processing. The system allows complex analysis and sensor fusion. Some scenarios based on sound processing, image collection, and accelerometers are presented. Power management was implemented which allowed the system to achieve energy neutrality in an outdoor deployment with a 525 × 345 mm solar panel.
In this paper, a distributed adaptive partitioning algorithm inspired by division of labor in honeybees is investigated for its applicability in a swarm of underwater robots in one hand and is qualitatively compared with the behavior of honeybee colonies on the other hand. The algorithm, partitioning social inhibition (PSI), is based on local interactions and uses a simple logic inspired from age-polyethism and task allocation in honeybee colonies. The algorithm is analyzed in simulation and is successfully applied here to partition a swarm of underwater robots into groups demonstrating its adaptivity to changes and applicability in real world systems. In a turn towards the inspiration origins of the algorithm, three honeybee colonies are then studied for age-polyethism behaviors and the results are contrasted with a simulated swarm running the PSI algorithm. Similar effects are detected in both the biological and simulated swarms suggesting biological plausibility of the mechanisms employed by the artificial system.
Bees are subject to numerous pressures in the modern world. The abundance and diversity of flowers has declined, bees are chronically exposed to cocktails of agrochemicals, and they are simultaneously exposed to novel parasites accidentally spread by humans. Climate change is likely to exacerbate these problems in the future. Stressors do not act in isolation; for example pesticide exposure can impair both detoxification mechanisms and immune responses, rendering bees more susceptible to parasites. It seems certain that chronic exposure to multiple, interacting stressors is driving honey bee colony losses and declines of wild pollinators, but such interactions are not addressed by current regulatory procedures and studying these interactions experimentally poses a major challenge. In the meantime, taking steps to reduce stress on bees would seem prudent; incorporating flower-rich habitat into farmland, reducing pesticide use through adopting more sustainable farming methods, and enforcing effective quarantine measures on bee movements are all practical measures that should be adopted. Effective monitoring of wild pollinator populations is urgently needed to inform management strategies into the future.
Copyright © 2015, American Association for the Advancement of Science.
Pollination improves the yield of most crop species and contributes to one-third of global crop production, but comprehensive benefits including crop quality are still unknown. Hence, pollination is underestimated by international policies, which is particularly alarming in times of agricultural intensification and diminishing pollination services. In this study, exclusion experiments with strawberries showed bee pollination to improve fruit quality, quantity and market value compared with wind and self-pollination. Bee-pollinated fruits were heavier, had less malformations and reached higher commercial grades. They had increased redness and reduced sugar-acid-ratios and were firmer, thus improving the commercially important shelf life. Longer shelf life reduced fruit loss by at least 11%. This is accounting for 0.32 billion US provided by bee pollination to the total value of 2.90 billion US$ made with strawberry selling in the European Union 2009. The fruit quality and yield effects are driven by the pollination-mediated production of hormonal growth regulators, which occur in several pollination-dependent crops. Thus, our comprehensive findings should be transferable to a wide range of crops and demonstrate bee pollination to be a hitherto underestimated but vital and economically important determinant of fruit quality.
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.
This paper describes the newly started EU-funded FP7 project ASSISI|bf, which deals with mixed societies: A honeybee society integrated with a group of stationary and interacting autonomous robotic nodes and a group of fish integrated in a society of autonomous moving robots.
In this article we describe the re-embodiment of biological aggregation behavior of honeybees in Jasmine micro-robots. The observed insect behavior, in the context of the insect's sensor—actor system, is formalized as behavioral and motion-sensing meta-models. These meta-models are transformed into a sensor—actor system of micro-robots by means of a sensors virtualization technique. This allows us to keep the efficiency and scalability of the bio-inspired approach. We also demonstrate the systematic character of this re-embodiment procedure on collective aggregation in a real robotic swarm.
Understanding the fundaments of colony losses and improving the status of colony health will require cross-cutting research initiatives including honeybee pathology, chemistry, genetics and apicultural extension. The 7th framework of the European Union requested research to empirically and experimentally fill knowledge gaps on honeybee pests and diseases, including ’Colony Collapse Disorder’ and the impact of parasites, pathogens and pesticides on honeybee mortality. The interactions among these drivers of colony loss will be studied in different European regions, using experimental model systems including selected parasites (e.g. Nosema and Varroa mites), viruses (Deformed Wing Virus, Black Queen Cell Virus, Israeli Acute Paralysis Virus) and model pesticides (thiacloprid, τ-fluvalinate). Transcriptome analyses will be used to explore host-pathogen-pesticide interactions and identify novel genes for disease resistance. Special attention will be given to sublethal and chronic exposure to pesticides and will screen how apicultural practices affect colony health. Novel diagnostic screening methods and sustainable concepts for disease prevention will be developed resulting in new treatments and selection tools for resistant stock. Research initiatives will be linked to various national and international ongoing European, North- and South-American colony health monitoring and research programs, to ensure a global transfer of results to apicultural practice in the world community of beekeepers.
Interacting with a specific animal society by integrating autonomous robot/s into the society, has become a powerful method to influence the behaviour of animals and investigate collective behaviour of both, animal and robot societies. In order to interact with animals, artificial unit/s should be well integrated into their society. In the European project ASSISIbf, a network of static autonomous robots called CASUs (Combined Actuator Sensor Units) for interaction with young honeybees has been designed. In the proposed approach CASUs can affect honeybees using three types of physical stimuli: heat, vibration and light. To provide feedback signals necessary for controlling CASU interaction with honeybees, accurate and reliable measurements of the stimuli are necessary. This paper describes the mechanical and electronic design of CASUs, capable of emitting controllable heat, vibration and light stimulations. Each CASU is equipped with temperature sensors, 3-axis accelerometers, infrared proximity sensors and microcontroller for data processing. Preliminary experimental results with honeybee groups are presented.
Eine Anordnung wird beschrieben, durch die Bienen auf warme Rume dressiert werden knnen; es ist fr den Erfolg gleichgltig ob die Dressurtemperatur ber oder unter der Unterscheidungstemperatur gewhlt wird. Ein Temperaturunterschied von 2 C wurde bei der verwendeten Anordnung von den Bienen eben noch im Gedchtnis behalten. Die Gre dieses Temperaturunterschieds blieb im untersuchten Bereich von der verwendeten Dressurtemperatur (20, 25, 32, 36 C) unabhngig.Im gestreckten Temperaturgeflle suchen Bienen eine bestimmte Zone auf (Thermopraeferendum). Im Winter entnommene Stockbienen (gestrtes Volk) stellten sich im Mittel auf 32,8 C ein. Nach lngerem vorherigen Aufenthalt bei tiefen Temperaturen (13,7; 14,7 C) war das Thermopraeferendum erniedrigt. Verschiedene Beleuchtung blieb ohne Einflu. Der relativen Inkonstanz winterlicher Stoektemperaturen (Himmer, Hess) scheint die Breite und relative Inkonstanz des Thermopraeferendums zu entsprechen. Jungbienen bis zum 7. Alterstag stellen sich sehr przise auf eine der Brutnestwrme entsprechende Temperatur ein (35,1–37,5 C). Sie behielten sie im untersuchten Fall bis zum 7. Tag, unabhngig vom Stockzustand (Brut-keine Brut) bei. Zwischen Alter der Bienen, Stockzustand (Brut) und Hhe der bevorzugten Temperatur scheinen gesetzmige Beziehungen zu bestehen. 24stndiger Hunger erhht an 2tgigen Jungbienen das Thermopraeferendum; eine vor dem Einsetzen mitgemachte CO2-Narkose senkt sie, whrend Licht bzw. Dunkelheit sie nicht beeinflussen.Bei einer langsamen Verschiebung des Wrmegeflles folgen die Bienen nach Abkhlung um 0,25 C der genderten Lage des Thermopraeferendums. Auf Erwrmung sprechen die Bienen nicht in gleicher Weise an; sie weichen erst vor der Schreckgrenze zurck.Die thermotaktisch bedeutsamen Rezeptoren befinden sich vor allem auf den letzten 5 Antennengliedern. Da aber auch antennenlose Bienen teilweise noch das Thermopraeferendum finden, knnen die gesuchten Sinnesorgane nicht nur an den Fhlern lokalisiert sein.Eine Orientierung der Bienen nach Wrmestrahlen lie sich weder durch Dressur-, noch in Spontanwahlversuchen, noch durch Beobachtung der Tnze im Ultrarot nachweisen. Es ist also nicht anzunehmen, da ihre Fhigkeit, auch bei bedecktem Himmel den Sonnenstand zu erkennen, auf der Wahrnehmung ihrer Wrmestrahlung beruht.
Pheromone trails laid by foraging ants serve as a positive feedback mechanism for the sharing of information about food sources. This feedback is nonlinear, in that ants do not react in a proportionate manner to the amount of pheromone deposited. Instead, strong trails elicit disproportionately stronger responses than weak trails. Such nonlinearity has important implications for how a colony distributes its workforce, when confronted with a choice of food sources. We investigated how colonies of the Pharaoh's ant, Monomorium pharaonis, distribute their workforce when offered a choice of two food sources of differing energetic value. By developing a nonlinear differential equation model of trail foraging, and comparing model with experiments, we examined how the ants allocate their workforce between the two food sources. In this allocation, the most profitable feeder (i.e. the feeder with the highest concentration of sugar syrup) was usually exploited by the majority of ants. The particular form of the nonlinear feedback in trail foraging means that when we offered the ants a choice between two feeders of equal profitability, foraging was biased to the feeder with the highest initial number of visitors. Taken together, our experiments illuminate how pheromones provide a mechanism whereby ants can efficiently allocate their workforce among the available food sources without centralized control.
In a remarkable example of collective decision-making, swarms of honeybees, Apis mellifera, choose one of many nest sites discovered and reported by their scouts. At first, dancing scouts communicate the location of many sites, but within a few days all dances focus on the same high-quality site. Instead of swarms acquiring global information by direct comparison of sites, , we find that the swarm's decision arises through a self-organized process driven by the dynamics of interacting individuals following simple rules based on local information.
For more than 50 years, investigators of the honey bee's waggle dance have reported that richer food sources seem to elicit longer-lasting and livelier dances than do poorer sources. However, no one had measured both dance duration and liveliness as a function of food-source profitability. Using video analysis, we found that nectar foragers adjust both the duration (D) and the rate (R) of waggle-run production, thereby tuning the number of waggle runs produced per foraging trip (W, where W= DR) as a function of food-source profitability. Both duration and rate of waggle-run production increase with rising food-source profitability. Moreover, we found that a dancing bee adjusts the rate of waggle-run production (R) in relation to food-source profitability by adjusting the mean duration of the return-phase portion of her dance circuits. This finding raises the possibility that bees can use return-phase duration as an index of food-source profitability. Finally, dances having different levels of liveliness have different mean durations of the return phase, indicating that dance liveliness can be quantified in terms of the time interval between consecutive waggle runs.
Farbensehen bei Insekten -ein rezeptorphysiologischer und neurophysiologischer Problemkreis
- R Menzel
R. Menzel, "Farbensehen bei Insekten -ein rezeptorphysiologischer und
neurophysiologischer Problemkreis," Verhandlungen der Deutschen
Zoologischen Gesellschaft pp. 26-40, 1977.
Coordination of collective behaviors in spatially separated agents
- R Mills
- P Zahadat
- F Silva
- D Mliklic
- P Mariano
- T Schmickl
- L Correia
R. Mills, P. Zahadat, F. Silva, D. Mliklic, P. Mariano, T. Schmickl, and
L. Correia, "Coordination of collective behaviors in spatially separated
agents," Proceedings of ECAL, pp. 579-586, 2015
A robotic system for researching social integration in honeybees
- K Griparic
- T Haus
- D Miklie
- M Polic
- S Bogdan