Towards new frontiers for distributed environmental monitoring based on an ecosystem of plant seed-like soft robots

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, to perform a low-cost, environmentally responsible, in-situ detection of key environmental parameters in air and topsoil.

Seeds dispersal strategies are driven by the spontaneous, often reversible, anisotropic deformation of their tissues upon swelling or drying of cell walls, and do not require any control or internal energy supply.Following a bioinspired design, these skills will be implemented into seed-like robots by using multi-functional biodegradable materials and natural morphological features, which endow them with the ability to fly and disperse via natural vector-mediated diffusion in air, or to respond to humidity variations to move on terrain surface and self-penetrate in topsoil.

Sensing will be based on diffusion, absorption, and structural changes of parts of the robots made from sensor materials with a chemical transduction mechanism and fluorescence-based optical readout. Recordings of temperature, humidity, CO2 and mercury (Hg2+) will be executed at air-soil interface. Deployment of the I-Seed robots and reading of the acquired measurements will be performed using drones.

Seed-drone communication will rely on optical signaling and fluorescence based on Light Detection and Ranging technology (fLiDAR), and using photonic ""tags"" to impart different type of sensors and robots a unique response.

Algorithms will be developed such that drones can map the robots in air and on the ground.By merging bioinspired soft robotics, material science, nanocomposite technologies, and environmental science, I-Seed will address the specific challenge by building a radically new dynamic scenario for analyzing and monitoring air and topsoil environments and their interface, extending environmental sensor networks and filling existing gaps of data analysis systems.