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.

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.

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.