Rajewicz Wiktoria, Romano Donato, Varughese Joshua Cherian, Vuuren Godfried Jansen Van, Campo Alexandre, Thenius Ronald, Schmickl Thomas
649 Institute of Biology, Graz, 8010, Austria.
University of Graz, Graz, Austria.
Biol Cybern. 2021 Dec;115(6):615-628. doi: 10.1007/s00422-021-00902-9. Epub 2021 Nov 23.
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".
面对水质迅速恶化的威胁,迫切需要开发新方法来监测全球水资源供应并尽早发现环境波动。全球变暖、城市扩张等因素不仅威胁着淡水供应,也危及许多栖息其中的物种的生存。传统上,基于实验室的研究既耗时又费钱,因此,开发一种实时、连续的监测方法已被证明是必要的。利用自主实现自我的实体成为监测环境的一种有效方式。将研究微生物燃料电池(MFC)作为一种替代能源,以使这些实体能够自我实现。通过在一种名为“生物混合体”的结构中使用各种生命形式作为生物传感器的角色,这一概念得到了改进,我们旨在依靠动物与机器人的相互作用在“Robocoenosis”项目框架内进一步发展这一结构。我们引入了一种全新概念,即具有各种生命形式作为生物传感器角色的完全自主生物混合体。在此,我们确定了在水下机器人领域最具潜力的生物,其中包括多毛贻贝、天鹅绒蚶、水蚤属以及各种藻类。特别关注基于它们与电子部件相互作用的“生态系统破解”。本项目以奥地利不同营养水平的湖泊(米尔施泰特湖、哈尔施塔特湖和新锡德尔湖)为案例研究,并作为“概念验证”。