Periodic Reporting for period 2 - I-Seed (Towards new frontiers for distributed environmental monitoring based on an ecosystem of plant seed-like soft robots)
Reporting period: 2022-07-01 to 2023-12-31
Understanding and monitoring natural ecosystems is necessary for an efficient implementation of sustainable strategies to tackle climate and environmental-related challenges. A long-standing challenge for environmental monitoring is the low spatial and temporal resolution of available data for many regions. Additionally, new approaches for the design of sustainable technologies are urgently needed to reduce current problems related to energy costs and e-waste produced.
The EU FET Proactive Environmental Intelligence project “I-Seed” 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, 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, as well as in their environmentally friendly design because made of 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 monitoring data are limited or no available, and thus for extending current environmental sensor frameworks and data analysis systems.
The EU FET Proactive Environmental Intelligence project “I-Seed” 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, 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, as well as in their environmentally friendly design because made of 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 monitoring data are limited or no available, and thus for extending current environmental sensor frameworks and data analysis systems.
The project started with the definition of the I-Seed environmental scenarios and field validation strategy, followed by a series of scientific and technical activities for the design and development of the I-Seed platform. Specifically, a focused study of plant seed materials and biomechanics was carried out in order to define useful specifications for the modelling and the design of the artificial systems in terms of multi-functional materials, their structural properties, and morphological adaptation. In parallel, we carried out activities for the mathematical modelling of movements of natural and artificial seeds, for the design and development of the artificial seeds body and their sensing material, for the development of the active laser-induced fluorescence system on the drone, as well as for the geo-referencing software and smart flight controller.
Two first series of seed-like robots were developed by taking inspiration from the seeds of Pelargonium appendiculatum (Cecchini et al., 2023; https://doi.org/10.1002/advs.202205146) and from the seeds of Acer campestre (Cikalleshi et al., 2023; https://doi.org/10.1126/sciadv.adi8492). The first type is a 4D printed artificial “crawling” seed made of biodegradable hygroscopic active material that allows it to move following environmental humidity changes and explore the soil. The second type is a 3D printed flyer that integrates a fluorescent sensing material for temperature readings.
The project is currently under the integration phase, in which the artificial seeds will be integrated with different sensing materials to be red and analysed by the drones, as planned in the overall scenario (attached figure).
Two first series of seed-like robots were developed by taking inspiration from the seeds of Pelargonium appendiculatum (Cecchini et al., 2023; https://doi.org/10.1002/advs.202205146) and from the seeds of Acer campestre (Cikalleshi et al., 2023; https://doi.org/10.1126/sciadv.adi8492). The first type is a 4D printed artificial “crawling” seed made of biodegradable hygroscopic active material that allows it to move following environmental humidity changes and explore the soil. The second type is a 3D printed flyer that integrates a fluorescent sensing material for temperature readings.
The project is currently under the integration phase, in which the artificial seeds will be integrated with different sensing materials to be red and analysed by the drones, as planned in the overall scenario (attached figure).
One of the first challenges was to define the methodology to translate the biological principles of some selected plant seeds and their dispersal strategies in the design and fabrication of environmentally responsive seed-like soft robots made of multi-functional biodegradable materials.
With the I-Seed objective to perform measurements in both air above soil and topsoil, two groups of natural seeds have been identified and analysed to extract biological specifications for the artificial systems: (1) self-burying seeds able to passively explore and penetrate the soil fractures. Crawling and burying occur thanks to the seed awn unit, which has hygroscopic characteristics and responds to variations of external humidity by changing its configuration; (2) flying seeds - which use their morphology and structural features to be carried by the wind and dispersed over great distances.
Starting from the identified biological principles, the second challenge focused on building artificial seed-like robots with biodegradable/environmentally-friendly materials able to provide structural support and dynamically respond to several environmental stimuli.
Motion and dispersion of the seeds can be obtained by using the natural combination of sensing and actuation through material computation, with which is possible to obtain a passive mobility (with no need of any internal energy source), exploiting their morphology, structure, and biomechanics/aerodynamics.
For the design, a modelling of the mechanical functions of biological and robotic seeds is useful. The challenge is to formulate new reduced models of fluid-structure-interaction in order to assess and optimize the role of shape and elastic compliance in selecting the flying style and controlling trajectories and flight performance. Models can also be used for resolving the mechanics and energetics of interaction, study the motion of seeds in contact with the soil, and optimize the performance of the robotic seeds.
Sensing in artificial seeds is obtained via transduction-based sensor materials, which challenge to advance in-situ sensing technology based on chemical transduction mechanisms. This goes beyond the current sensor network by using materials that react to environmental parameters, such as temperature or humidity, or to certain chemical analytes by changing optical properties. Reading of the signal is based on optical signaling and fluorescence by LiDAR (Light Detection and Ranging) technology. The challenge focuses on the design and development of a multi-wavelength fluorescence LiDAR system capable of detecting several excitations in one observation. This will extend the presented laser-induced fluorescence principle as evaluated for vegetation to other materials being part of the I-Seeds.
LiDAR data post-processing and drone flight controller are necessary to design and implement a “smart” flight controller based on deep learning architecture, with a software able to read and process in real-time the data stream and a desktop software to do post-processing and export.
The collection of environmental data and their analysis target the filling of geographical gaps to improve ongoing monitoring networks in areas where no monitoring infrastructures are available with low investment and management costs. I-Seed scenario challenges to increase the spatial resolution of monitoring points/sites developing a low-cost technology allowing to execute continuous field campaigns in contaminated sites/emission regions to cross-check the effectiveness of remediation measures adopted to restore ecosystems quality.
With the I-Seed objective to perform measurements in both air above soil and topsoil, two groups of natural seeds have been identified and analysed to extract biological specifications for the artificial systems: (1) self-burying seeds able to passively explore and penetrate the soil fractures. Crawling and burying occur thanks to the seed awn unit, which has hygroscopic characteristics and responds to variations of external humidity by changing its configuration; (2) flying seeds - which use their morphology and structural features to be carried by the wind and dispersed over great distances.
Starting from the identified biological principles, the second challenge focused on building artificial seed-like robots with biodegradable/environmentally-friendly materials able to provide structural support and dynamically respond to several environmental stimuli.
Motion and dispersion of the seeds can be obtained by using the natural combination of sensing and actuation through material computation, with which is possible to obtain a passive mobility (with no need of any internal energy source), exploiting their morphology, structure, and biomechanics/aerodynamics.
For the design, a modelling of the mechanical functions of biological and robotic seeds is useful. The challenge is to formulate new reduced models of fluid-structure-interaction in order to assess and optimize the role of shape and elastic compliance in selecting the flying style and controlling trajectories and flight performance. Models can also be used for resolving the mechanics and energetics of interaction, study the motion of seeds in contact with the soil, and optimize the performance of the robotic seeds.
Sensing in artificial seeds is obtained via transduction-based sensor materials, which challenge to advance in-situ sensing technology based on chemical transduction mechanisms. This goes beyond the current sensor network by using materials that react to environmental parameters, such as temperature or humidity, or to certain chemical analytes by changing optical properties. Reading of the signal is based on optical signaling and fluorescence by LiDAR (Light Detection and Ranging) technology. The challenge focuses on the design and development of a multi-wavelength fluorescence LiDAR system capable of detecting several excitations in one observation. This will extend the presented laser-induced fluorescence principle as evaluated for vegetation to other materials being part of the I-Seeds.
LiDAR data post-processing and drone flight controller are necessary to design and implement a “smart” flight controller based on deep learning architecture, with a software able to read and process in real-time the data stream and a desktop software to do post-processing and export.
The collection of environmental data and their analysis target the filling of geographical gaps to improve ongoing monitoring networks in areas where no monitoring infrastructures are available with low investment and management costs. I-Seed scenario challenges to increase the spatial resolution of monitoring points/sites developing a low-cost technology allowing to execute continuous field campaigns in contaminated sites/emission regions to cross-check the effectiveness of remediation measures adopted to restore ecosystems quality.