Barbara Mazzolai
ABSTRACT
Understanding and monitoring natural ecosystems is necessary for an efficient implementation of sustainable strategies to tackle climate and environmental-related challenges, such as: protect and improve the quality of air, water, and soil; safeguard species biodiversity; and effectively manage natural resources. A longstanding challenge for environmental monitoring is the low spatial and temporal resolution of available data for many regions. Also, new approaches for the design of sustainable technologies is urgently needed to reduce current problems related to energy costs and e-waste produced.
With this in mind, 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 analysing and monitoring topsoil and air. 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 a low-cost, environmentally responsible, and in-situ detection. The natural functional mechanisms of seeds dispersal offer a rich source of robust, highly adaptive, mass and energy efficient mechanisms, and behavioural 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.
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