Improving the Accuracy of a Biohybrid for Environmental Monitoring.

Environmental monitoring should be minimally disruptive to the ecosystems that it is embedded in. Therefore, the project Robocoenosis suggests using biohybrids that blend into ecosystems and use life forms as sensors. However, such a biohybrid has limitations regarding memory-as well as power-capacities, and can only sample a limited number of organisms. We model the biohybrid and study the degree of accuracy that can be achieved by using a limited sample. Importantly, we consider potential misclassification errors (false positives and false negatives) that lower accuracy. We suggest the method of using two algorithms and pooling their estimations as a possible way of increasing the accuracy of the biohybrid. We show in simulation that a biohybrid could improve the accuracy of its diagnosis by doing so. The model suggests that for the estimation of the population rate of spinning Daphnia, two suboptimal algorithms for spinning detection outperform one qualitatively better algorithm. Further, the method of combining two estimations reduces the number of false negatives reported by the biohybrid, which we consider important in the context of detecting environmental catastrophes. Our method could improve environmental modeling in and outside of projects such as Robocoenosis and may find use in other fields.

Freshwater organisms potentially useful as biosensors and power-generation mediators in biohybrid robotics.

Facing the threat of rapidly worsening water quality, there is an urgent need to develop novel approaches of monitoring its global supplies and early detection of environmental fluctuations. Global warming, urban growth and other factors have threatened not only the freshwater supply but also the well-being of many species inhabiting it. Traditionally, laboratory-based studies can be both time and money consuming and so, the development of a real-time, continuous monitoring method has proven necessary. The use of autonomous, self-actualizing entities became an efficient way of monitoring the environment. The Microbial Fuel Cells (MFC) will be investigated as an alternative energy source to allow for these entities to self-actualize. This concept has been improved with the use of various lifeforms in the role of biosensors in a structure called "biohybrid" which we aim to develop further within the framework of project Robocoenosis relying on animal-robot interaction. We introduce a novel concept of a fully autonomous biohybrid agent with various lifeforms in the role of biosensors. Herein, we identify most promising organisms in the context of underwater robotics, among others Dreissena polymorpha, Anodonta cygnaea, Daphnia sp. and various algae. Special focus is placed on the "ecosystem hacking" based on their interaction with the electronic parts. This project uses Austrian lakes of various trophic levels (Millstättersee, Hallstättersee and Neusiedlersee) as case studies and as a "proof of concept".

Algorithmic requirements for swarm intelligence in differently coupled collective systems.

Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments.

Daphnia's phototaxis as an indicator in ecotoxicological studies: A review.

Animal-based sensors have been increasingly applied to many water monitoring systems and ecological studies. One of the staple organisms used as living sensors for such systems is Daphnia. This organism has been extensively studied and, with time, used in many toxicological and pharmaceutical bioassays, often used for exploring the ecology of freshwater communities. One of its behaviours used for evaluating the state of the aquatic environment is phototaxis. A disruption in the predicted behaviour is interpreted as a sign of stress and forms the basis for further investigation. However, phototaxis is a result of complex processes counteracting and interacting with each other. Predator presence, food quality, body pigmentation and other factors can greatly affect the predicted phototactic response, hampering its reliability as a bioindicator. Therefore, a holistic approach and meticulous documentation of the methods are needed for the correct interpretation of this behavioural indicator. In this review, we present the current methods used for studying phototaxis, the factors affecting it and proposed ways to optimise the reliability of the results.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

A swarm design paradigm unifying swarm behaviors using minimalistic communication.

Numerous nature inspired algorithms have been suggested to enable robotic swarms, mobile sensor networks and other multi-agent systems to exhibit various self-organized behaviors. Swarm intelligence and swarm robotics research have been underway for a few decades and have produced many such algorithms based on natural self-organizing systems. While a large body of research exists for variations and modifications in swarm intelligence algorithms, there have been few attempts to unify the underlying agent level design of these widely varying behaviors. In this work, a design paradigm for a swarm of agents is presented which can exhibit a wide range of collective behaviors at swarm level while using minimalistic single-bit communication at the agent level. The communication in the proposed paradigm is based on waves of 'ping'-signals inspired by strategies for communication and self organization of slime mold (Dictyostelium discoideum) and fireflies (lampyridae). The unification of common collective behaviors through this Wave Oriented Swarm Paradigm (WOSP) enables the control of swarms with minimalistic communication and yet allowing the emergence of diverse complex behaviors. It is demonstrated both in simulation and using real robotic experiments that even a single-bit communication channel between agents suffices for the design of a substantial set of behaviors. Ultimately, the reader will be enabled to combine different behaviours based on the paradigm to develop a control scheme for individual swarms.

From honeybees to robots and back: division of labour based on partitioning social inhibition.

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.

Dynamics of collective decision making of honeybees in complex temperature fields.

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.