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A cellular model of swarm intelligence in bees and robots
... To analyze the behavior of the bees in this mixed society we use a simple cellular-automaton inspired by the BEECLUST algorithm [16] (Fig. 1c), an algorithmic honeybee behavioral model that was first introduced in [17]. We implement autonomous control and close the loop between the animals and the robots. ...
... Here we show an updated version of our two-dimensional cellular model, first introduced in [17]. In contrast to the original model we here introduce immobile robots, the CASUs, as additional agents to the model: Each CASU can autonomously regulate its own temperature in the model cells around it as a reaction of sensing local cells filled with bees. ...
... The model includes three free parameters (α, a parameter that influences the random walk behavior of the agents, σ, a parameter that represents the social influence of neighboring bees and ᴪ, a parameter that represents the probability that a bee switches into resting mode). Since α has already been extensively treated in [17], this section focuses on the newly introduced parameter ᴪ and its influence on σ. Fig. 3a and 3c reacted according to the positive feedback loop, resulting in a winning CASU (warmer, surrounded by bees) and a losing CASU (colder, surrounded by almost no bees). ...
... On the other hand, even such an extended model can still exhibit a low variance in its predictions, due to the implicit base assumptions of ODE models in principle, such as the assumption of optimal mixing and distribution of the modelled agents in space within the areas modelled by each system variable. In this case, a step to spatially explicit individual-based models and spatially more heterogeneous models, like cellular automata (Szopek et al., 2017) or multi-agent models (Stefanec et al., 2017b), might be more suitable to capture the effects of higher variances that are often observed in natural, and thus physically manifested, systems. ...
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.
... 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]. ...
Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs). Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents) in the arena (environment)”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.
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.
Agents controlled by a swarm algorithm interact with each other so that they have collective capabilities that a sin-gle agent does not have. The bio-inspired swarm-algorithm "BEECLUST" has the aim to aggregate a swarm at the global optimum even if there are several local optima (of the same type) present. But what about gradients produced of differ-ent stimulus types? In this paper, we present the concept of "social stimuli". We investigate how robots controlled by the BEECLUST-algorithm react to a social stimulus which is created by placing immobilized robots in the environ-ment. It shows that the robots controlled by the BEECLUST-algorithm are able to react on a social stimulus within an en-vironment with a global and a local optimum.
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.
In this work we investigated how robust a robot swarm acts against disturbances caused by another robot swarm, both using the BEECLUST algorithm, which is inspired by honeybee behaviour. For our investigation we simulated an environment with an ambient illuminance, a light spot and a shadow spot. In such an environment we tested two different castes of Jasmine III robots whereas each caste had to perform a different task. One swarm aggregates at places of high illuminance (light spot) and the other one at places of low illuminance (shadow spot). We show that small swarm populations can benefit from the presence of another robot swarm. Medium populated swarms are affected neither positively nor negatively. Large swarm populations act robust against disturbances caused by other robot swarms as long as no jamming effects occur. In this article we show that the BEECLUST algorithm provides all features for making collective decisions. Furthermore we show that the robustness of the BEECLUST algorithm allows us to control a heterogeneous robot swarm in environments which demand differing controller strategies and swarm intelligent behaviour.
1.
Populations of honey bees held at a constant temperature for 24–48 h exhibited diurnal rhythms of metabolic rate (MR) and locomotor activity with peaks during the day and lows at night. The amplitude of the metabolic rhythm decreased as air temperature (T
in) increased.
2.
Thermoregulatory behaviors including clustering and fanning occurred in cycles which correlated with the diurnal rhythms of MR and activity.
3.
At cold air temperature (10C), a high rate of thermoregulatory heat production was independent of visible activity, and conversely, at high air temperatures (40C), MR was low despite increased locomotor activity.
4.
Decreasing air temperature from 30 to 10C day and night resulted in clustering, and metabolic increases proportional to the degree of cooling. Raising air temperature from 30 to 40C day and night caused a drop in metabolic rate, an increase in locomotor activity, and fanning.
5.
Day/night differences in thermoregulatory responses to cooling included a nocturnal reduction of the threshold air temperature for thermogenesis and a decrease in the slopes of the metabolic response curves below this threshold. At 10C there was more clustering at night than during the day.
6.
The presence of capped brood moderates these responses in a quantitative manner, as indicated by the greater metabolic rate when the bee/brood ratio is small and the greater amount of clustering on brood comb than on broodless comb.
7.
At cold air temperatures (10C), capped brood temperature is maintained above 30C through the combined effects of clustering and thermogenesis. The metabolic responses are inversely correlated with the degree of clustering.
We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment.
This decision making is an emergent property of decentralized self-organization, which results from executing a very simple
bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment
and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although
no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied
the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation
behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic
swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust.
This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.
Searching an area of interest based on environmental cues is a challenging benchmark task for an autonomous robot. It gets even harder to achieve if the goal is to aggregate a whole swarm of robots at such a target site after exhaustive exploration of the whole environment. When searching gas leakages or heat sources, swarm robotic approaches have been evaluated in recent years, which were, in part, inspired by biologically motivated control algorithms. Here we present a bio-inspired control program for swarm robots, which collectively explore the environment for a heat source to aggregate. Behaviours of young honeybees were embodied on a robot by adding thermosensors in ‘virtual antennae’. This enables the robot to perform thermotaxis, which was evaluated in a comparative study of an egoistic versus a collective swarm approach.
We present results from an experiment similar to one performed by Packard [24], in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton's parameter [17], and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near "critical" values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to , dynamical-behavior classes, and computation in CA. The main constructive re...
There are many ways in which social insect for- agers may organise the collection of resources and their transportation back to the nest. One way is to partition the task into a number of sequential stages in which material is passed from one worker to another in a relay fashion. This relatively new concept is known as task partitioning. In this study, we focus on a particular form of task partitioning, bucket brigades, which we define as a multistage (i.e., three or more stages) partitioned transport scheme that uses only direct transfer between individual workers and without any predetermined transfer locations, other than the first or last stages. We first consider the potential costs and benefits of bucket brigades compared to other transportation schemes. We then use theory and computer simulation to analyse some of these aspects in detail. In one empirical study of a bucket brigade, foragers were generally found to be sequenced from smallest (near the food source) to fastest (nearest the nest). This exactly matches what dynamical systems theory would predict as an ergonomically efficient solution. However, we also demonstrate that a single and simple local rule - larger ants win fights over material - will generate this sequencing as an epiphenomenon that is not necessarily optimal. We use the behaviour of bucket brigades to reveal some general points about the optimality of task partitioning in more detail.
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.
Studies of large, randomly assembled binary (Boolean) node automata have demonstrated that such systems can spontaneously exhibit enormously ordered dynamical behavior. An important approach to characterizing these behaviors has consisted of studying ensembles of automata. Ensemble specifications have been based on: 1) choice of Boolean function regulating each node in the automaton; 2) random or biased mappings of the 2N automata states into themselves; 3) the numbers of inputs, K, per node. This article briefly reviews these approaches, reports new results on the expected behavior of threshold automata, and automata with a particular class of biased mappings. In addition, it discusses emergent, highly ordered dynamical behavior in random automata rich in a specific class of “canalizing” Boolean functions due to the crystallization of powerful subautomata called forcing structures. The ordered behaviors may be of importance in biological evolution, in physics, and in the design of adaptive automata.
Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.
A model of simple algorithmic "agents" acting in a discrete temperature field is used to investigate the movement of individuals in thermoregulating honey bee (Apis mellifera) clusters. Thermoregulation in over-wintering clusters is thought to be the result of individual bees attempting to regulate their own body temperatures. At ambient temperatures above 0( degrees )C, a clustering bee will move relative to its neighbours so as to put its local temperature within some ideal range. The proposed model incorporates this behaviour into an algorithm for bee agents moving on a two-dimensional lattice. Heat transport on the lattice is modelled by a discrete diffusion process. Computer simulation of this model demonstrates qualitative behaviour which agrees with that of real honey bee clusters. In particular, we observe the formation of both disc- and ring-like cluster shapes. The simulation also suggests that at lower ambient temperatures, clusters do not always have a stable shape but can oscillate between insulating rings of different sizes and densities.
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