Through the combination of artificial components and living organisms, we can develop a novel methodology for aquatic monitoring. By observing the responses of organisms to changes in their environment, a broad-spectrum sensor was created. One of the organisms broadly used as a biosensor is Daphnia . Its broad distribution and well-studied biology make it a promising element for incorporating into a biohybrid. This Daphnia -based sensor was calibrated against increasing salinity as a preliminary experiment. The swimming behaviour (spinning and movement inhibition) was observed for different salinities. The results showcase significant and observable differences. This and other calibration experiments will be used here as bases for the behavioural results interpretation.

Several anecdotal experimental observations suggest that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can considerably increase the diversity of results obtained by evolutionary computation methods and can even yield surprises.

We demonstrate here that the morphogenetic Primordial Particle System, which was originally defined for two dimensions only, can also operate with minimal adaptations in a three-dimensional setting, producing similar life-like structures and dynamics.

Biohybrids combine artificial robotic elements with living organisms. These novel technologies allow for obtaining useful data on the environment by implementing organisms as “living sensors”. Natural water resources are under serious ecological threat and there is always a need for new, more efficient methods for aquatic monitoring. Project Robocoenosis introduces the use of biohybrid entities as low-cost and long-term environmental monitoring devices. This will be done by combining lifeforms with technical parts which will be powered with the use of MFCs. This concept will allow for a more well-rounded data collection and provide an insight into the water body with minimal human impact.

We emulated an experiment that shows the Einstellung-effect by building an agent-based model and developed a new heuristic that helps to overcome the effect.

In the wake of climate change and water quality crisis, it is crucial to find novel ways to extensively monitor the environment and to detect ecological changes early. Biomonitoring has been found to be an effective way of observing the aggregate effect of environmental fluctuations. In this paper, we outline the development of biohybrids which will autonomously observe simple organisms (microorganisms, algae, mussels etc.) and draw conclusions about the state of the water body. These biohybrids will be used for continuous environmental monitoring and to detect sudden (anthropologically or ecologically catastrophic) events at an early stage. Our biohybrids are being developed within the framework of project Robocoenosis, where the operational area planned are Austrian lakes. Additionally, we discuss the possible use of various species found in these waters and strategies for biomonitoring. We present early prototypes of devices that are being developed for monitoring of organisms.

We used a model of honeybees’ decision-making to investigate the effect of robots on the hive's foraging decisions. We find that at least two robots are needed to make a significant impact.

In a time marked by ecological decay and by the perspective of a severe backlash of this ecosystem decay and climate devastation onto human society, bold moves that employ novel technology to counteract this decline are required. We present a novel concept of employing Artificial Life technology, in the form of cybernetically enhanced bio-hybrid superorganisms as a countermeasure and as a contingency plan. We describe our general conceptual paradigm, consisting of three interacting action plans, namely: (1) Organismic Augmentation; (2) Bio- Hybrid Socialization and (3) Ecosystem Hacking, which together compose a method to create a novel agent for ecosystem stabilization. We demonstrate, through early results from the research project HIVEOPOLIS, a specific way how classic Artificial Life technologies can create such a living, ecologically active and technologically-augmented superorganism that operates outside in the field. These technologies range from cellular automata and biomimetic robots to novel and sustainable biocompatible materials. Aiming at having a real-world impact on the society that relies on our biosphere is an important aspect in Artificial Life research and is fundamental to our methodology to create a physically embodied and useful form of Artificial Life.

Robot swarms are known to be robust to individual robot failures. However, a reduced swarm size causes a reduced swarm density. A too low swarm density may then decrease swarm performance, that should be compensated by adapting the individual behavior. Similarly, swarm behaviors can also be adapted to changes in the environment, such as dynamic light conditions. We study aggregation of swarm robots controlled by an extended variant of the BEECLUST algorithm. The robots are asked to aggregate at the brightest spot in their environment. Our approach efficiently adapts this swarm aggregation behavior to variability in swarm density and light conditions. First, each robot individually monitors its environment continuously by sampling its local swarm density and perceived light condition. Second, we exploit the collaboration of robots by letting them share features of these measurements with their neighbors by communication. In extensive robot swarm experiments with ten robots we validate our approach with dynamically changing swarm densities and under dynamic light conditions. We find an improved performance compared to robot swarms without communication and without awareness of the swarm density.

Mobile sensor networks and robotic swarms are being used for monitoring and exploring environments or environmental events due to the advantages offered by their distributed nature. However, coordination and self-organization of a large number of individuals is often costly in terms of energy and computation power, thus limiting the longevity of the distributed system. In this paper we present a bio-inspired algorithm enabling a robotic swarm to collectively detect anomalies in environmental parameters in a self-organized, reliable and energy efficient manner. Individuals in the swarm communicate via 1-bit signals to collectively confirm the detection of an anomaly while minimizing energy spent for communication and taking measurements. This algorithm is specifically designed for a swarm of underwater robots called “aMussels” to examine a phenomenon referred to as “anoxia” which results in oxygen depletion in the lagoon of Venice. We present the algorithm, conduct simulations and robotic experiments to examine the performance of the algorithm with respect to early detection of anoxia while minimizing energy consumption.

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 acts as a locally acting ‘stop signal’ for bees. 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 digital genotypes generally confirm fitness values obtained during evolution. We also tested the transferability of key genotypes to a single container, in which bees are free to visit one vibrating and two dummy robots. Encouragingly, most genotypes 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.

Morphogenesis in biological systems is controlled by the parameters encoded in the genomes and rules of interaction between different components of the system and environment. Several methods are proposed for developing morphology of artificial structures. Some of them are inspired by embryogenesis in biological organisms. Others use more abstract generative encodings such as variances of L-systems. Our approach to morphogenesis is based on the distribution of a common resource between competing components of a growing system. The novel distributed controller called Vascular Morphogenesis Controller (VMC) is inspired by the growth process of plants and more specifically the competition between different branches for developing vessels and thus for further growth. The initial algorithm is introduced for modular robots. Here we use it to solve a maze.

In this paper we report our ongoing work with evolving biohybrid societies. We develop robots that will be integrated in an animal society and will be accepted as a conspecific. Moreover, we want our robots to affect the behaviour of animals. We are using evolutionary algorithms to optimise robot controllers, where fitness is evaluated via measuring the effect a robot controller has on the animals. Several issues have to be considered: if the animals do not have a homogeneous behaviour several evaluations are needed to rule out outliers, and yet evaluating animal behaviour is a time consuming task. Besides the time it takes to record their behaviour, we have to take into account animal resting time, stimulus habituation, and feeding periods. Another factor that increases the task difficulty is robot heterogeneity, which is similar to the so called reality gap problem that occurs in evolving robot controllers in simulation. In our case, if we want a robust robot controller, we have to evaluate it in different robots. Overall, we found that doing online on-board evolutionary computation with robotic devices and animals is extremely challenging and we provide clues to avoid its major pitfalls.

This article presents a novel bio-inspired emergent gradient taxis principle for robot swarms. The underlying communication method was inspired by slime mold and fireflies. Nature showcases a number of simple organisms which can display complex behavior in various aspects of their lives such as signaling, foraging, mating etc. Such decentralized behaviors at the organism level gives rise to an emergent intelligence such as in bees, slime mold, fireflies etc. Chemo taxis and photo taxis are known to be abilities exhibited by simple organisms without elaborate sensory and actuation capabilities. Our novel algorithm combines the underlying principles of slime mold and fireflies to achieve gradient taxis purely based on neighbor-to- neighbor communication. In this article, we present a model of the algorithm and test the algorithm in a multiagent simulation environment.