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.

SummaryIn this BEEBOOK paper we present a set of established methods for quantifying honey bee behaviour. We start with general methods for preparing bees for behavioural assays. Then we introduce assays for quantifying sensory responsiveness to gustatory, visual and olfactory stimuli. Presentation of more complex behaviours like appetitive and aversive learning under controlled laboratory conditions and learning paradigms under free-flying conditions will allow the reader to investigate a large range of cognitive skills in honey bees. Honey bees are very sensitive to changing temperatures. We therefore present experiments which aim at analysing honey bee locomotion in temperature gradients. The complex flight behaviour of honey bees can be investigated under controlled conditions in the laboratory or with sophisticated technologies like harmonic radar or RFID in the field. These methods will be explained in detail in different sections. Honey bees are model organisms in behavioural biology for their complex yet plastic division of labour. To observe the daily behaviour of individual bees in a colony, classical observation hives are very useful. The setting up and use of typical observation hives will be the focus of another section. The honey bee dance language has important characteristics of a real language and has been the focus of numerous studies. We here discuss the background of the honey bee dance language and describe how it can be studied. Finally, the mating of a honey bee queen with drones is

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.

In this work we investigate spatial collective decision-making in a swarm of microrobots, inspired by the thermotactic aggregation behavior of honeybees. The sensing and navigation capabilities of these robots are intentionally limited; no digital sensor data processing and no direct communication are allowed. In this way, we can approximate the features of smaller mesoscopic-scale systems and demonstrate that even such a limited swarm is nonetheless able to exhibit simple forms of intelligent and adaptive collective behavior

SUMMARYKatydid receivers face the problem of detecting behaviourally relevant predatory cues from echolocating bats in the same frequency domain as their own conspecific mating signals. We therefore tested the hypothesis that katydids are able to detect the presence of insectivorous bats in spike discharges at early stages of nervous processing in the auditory pathway by using the temporal details characteristic for responses to echolocation sequences. Spike activity was recorded from an identified nerve cell (omega neuron) under both laboratory and field conditions. In the laboratory, the preparation was stimulated with sequences of bat calls at different repetition rates typical for the guild of insectivorous bats, in the presence of background noise. The omega cell fired brief high-frequency bursts of action potentials in response to each bat sound pulse. Repetition rates of 18 and 24Hz of these pulses resulted in a suppression of activity resulting from background noise, thus facilitating the detection of bat calls. The spike activity typical for responses to bat echolocation contrasts to responses to background noise, producing different distributions of inter-spike intervals. This allowed development of a 'neuronal bat detector' algorithm, optimized to detect responses to bats in afferent spike trains. The algorithm was applied to more than 24 hours of outdoor omega-recordings performed either at a rainforest clearing with high bat activity or in rainforest understory, where bat activity was low. In 95% of cases, the algorithm detected a bat reliably, even under high background noise, and correctly rejected responses when an electronic bat detector showed no response.