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Titel: Advancing environmental intelligence through novel approaches in soft bioinspired robotics and allied technologies
VerfasserIn: Mazzolai, Barbara
Kraus, Tobias
Pirrone, Nicola
Kooistra, Lammert
De Simone, Antonio
Cottin, Antoine
Margheri, Laura
Sprache: Englisch
Titel: Proceedings of the 2022 ACM Conference on Information Technology for Social Good
Seiten: 265-268
Verlag/Plattform: ACM
Erscheinungsjahr: 2022
Erscheinungsort: New York
Konferenzort: Limassol, Cyprus
DDC-Sachgruppe: 540 Chemie
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: The EU-funded FET Proactive Environmental Intelligence project “I-Seed” (Grant Agreement n. 101017940, https://www.iseedproject.eu/) targets towards the development of a radically simplified and environmentally friendly approach for environmental monitoring. Specifically, I-Seed aims at developing a new generation of self-deployable and biodegradable soft miniaturized robots, inspired by the morphology and dispersion abilities of plant seeds, able to perform low-cost, environmentally responsible, in-situ measurements. The natural functional mechanisms of seeds dispersal offer a rich source of robust, highly adaptive, mass and energy efficient mechanisms, and behavioral and morphological intelligence, which can be selected and implemented for advanced, but simple, technological inventions. I-Seed robots are conceived as unique in their movement abilities because inspired by passive mechanisms and materials of natural seeds, and unique in their environmentally friendly design because made of all biodegradable components. Sensing is based on a chemical transduction mechanism in a stimulus-responsive sensor material with fluorescence-based optical readout, which can be read via one or more drones equipped with fluorescent LiDAR technology and a software able to perform a real time georeferencing of data. The I-Seed robotic ecosystem is envisioned to be used for collecting environmental data in-situ with high spatial and temporal resolution across large remote areas where no monitoring data are available, and thus for extending current environmental sensor frameworks and data analysis systems.
DOI der Erstveröffentlichung: 10.1145/3524458.3547262
URL der Erstveröffentlichung: https://dl.acm.org/doi/10.1145/3524458.3547262
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-376744
hdl:20.500.11880/34216
http://dx.doi.org/10.22028/D291-37674
ISBN: 978-1-4503-9284-6
Datum des Eintrags: 7-Nov-2022
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Chemie
Professur: NT - Prof. Dr. Tobias Kraus
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons