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doi:10.22028/D291-37674
Title: | Advancing environmental intelligence through novel approaches in soft bioinspired robotics and allied technologies |
Author(s): | Mazzolai, Barbara Kraus, Tobias Pirrone, Nicola Kooistra, Lammert De Simone, Antonio Cottin, Antoine Margheri, Laura |
Language: | English |
Title: | Proceedings of the 2022 ACM Conference on Information Technology for Social Good |
Pages: | 265-268 |
Publisher/Platform: | ACM |
Year of Publication: | 2022 |
Place of publication: | New York |
Place of the conference: | Limassol, Cyprus |
DDC notations: | 540 Chemistry |
Publikation type: | Conference Paper |
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 of the first publication: | 10.1145/3524458.3547262 |
URL of the first publication: | https://dl.acm.org/doi/10.1145/3524458.3547262 |
Link to this record: | 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 |
Date of registration: | 7-Nov-2022 |
Faculty: | NT - Naturwissenschaftlich- Technische Fakultät |
Department: | NT - Chemie |
Professorship: | NT - Prof. Dr. Tobias Kraus |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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