Study and development of a magnetic steering system for microrobots

In a close future micro-scaled untethered robots might be able to access small spaces inside the human body, currently reachable only by using invasive surgical methods, thus revolutionizing future medicine. The aim of this Master Thesis work is to study and develop a system that can exploit static magnetic fields and gradients to steer purpose-developed microrobots. A concept of the device for the generation of magnetic fields is first elaborated, moving from the state-of-art systems based on Helmholtz and Maxwell coils, which can generate, respectively, nearly uniform magnetic fields and gradients. A uniform magnetic field can be used to orient a magnetic or magnetisable object, aligning it with the direction of the field, while a uniform magnetic gradient can be used to shift such an object. The developed system is formed by two coils in the Maxwell geometrical configuration and independently powered in order to generate a uniform magnetic gradient, a quasi-uniform magnetic field or a superimposition of the two, reducing the overall complexity of the hardware with respect to the systems also employing Helmholtz coils. An analytical model of the on-axis magnetic field generated by the device and a finite element model of the field in the workspace are developed. Three microrobot prototypes are then considered: a millimetre-sized NdFeB cylindrical permanent magnet, which allows to test the maximum performances of the developed device, a polymeric microbead, which is more compatible with biomedical applications but less reactive to magnetic fields than a permanent magnet, and a polymeric nanofilm, which allows to test the steering of very anisotropic shapes, both containing iron oxide nanoparticles. Models of their interaction with magnetic fields are presented. Furthermore, a model of the motion of the three prototypes employing the developed magnetic device is presented. The experimental set up is described, including the two coils and their support backing, the monitoring and powering circuitry and a software kit containing four graphical user interfaces for the calibration and validation of the system. After a set of trials performed for the calibration of the magnetic-field-generating device, the system is tested in steering the microrobot prototypes. The extrapolated data are compared to the behaviours predicted by the magnetic motion models. The abilities of the magnetic steering system and its main limits are finally examined, suggesting possible improvements of both the magnetic device and the microrobots in order to enhance their control and manipulation. In particular indications for developing the next-generation of wireless magnetically-actuated microrobots and the relative steering systems are extrapolated

Gait learning for soft microrobots controlled by light fields

Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing conditions. Albeit, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semi-synthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot's locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on light-controlled soft microrobots and probabilistic learning control.Comment: 8 pages, 7 figures, to appear in the proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 201

3D-printed hierarchical arrangements of actuators mimicking biological muscular architectures

: Being able to imitate the sophisticated muscular architectures that characterize the animal kingdom in biomimetic machines would allow them to perform articulated movements with the same naturalness. In soft robotics, multiple actuation technologies have been developed to mimic the contraction of a single natural muscle, but a few of them can be implemented in complex architectures capable of diversifying deformations and forces. In this work, we present three different biomimetic muscle architectures, i.e., fusiform, parallel, and bipennate, which are based on hierarchical arrangements of multiple pneumatic actuators. These biomimetic architectures are monolithic structures composed of thirty-six pneumatic actuators each, directly 3D printed through low-cost printers and commercial materials without any assembly phase. The considerable number of actuators involved enabled the adoption and consequent comparison of two regulation strategies: one based on input modulation, commonly adopted in pneumatic systems, and one based on fiber recruitment, mimicking the regulation behavior of natural muscles. The straightforward realization through additive manufacturing processes of muscle architectures regulated by fiber recruitment strategies facilitates the development of articulated muscular systems for biomimetics machines increasingly similar to the natural ones

Electricity markets: Designing auctions where suppliers have uncertain costs

We analyse how the market design influences the bidding behaviour in multi-unit auctions, such as wholesale electricity markets. It is shown that competition improves for increased market transparency and we identify circumstances where the auctioneer prefers uniform to discriminatory pricing. We note that political risks could significantly worsen competition in hydro-dominated markets. It would be beneficial for such markets to have clearly defined contingency plans for extreme market situations.market transparenc

A Power-efficient Propulsion Method for Magnetic Microrobots

Current magnetic systems for microrobotic navigation consist of assemblies of electromagnets, which allow for the wireless accurate steering and propulsion of sub-millimetric bodies. However, large numbers of windings and/or high currents are needed in order to generate suitable magnetic fields and gradients. This means that magnetic navigation systems are typically cumbersome and require a lot of power, thus limiting their application fields. In this paper, we propose a novel propulsion method that is able to dramatically reduce the power demand of such systems. This propulsion method was conceived for navigation systems that achieve propulsion by pulling microrobots with magnetic gradients. We compare this power-efficient propulsion method with the traditional pulling propulsion, in the case of a microrobot swimming in a micro-structured confined liquid environment. Results show that both methods are equivalent in terms of accuracy and the velocity of the motion of the microrobots, while the new approach requires only one ninth of the power needed to generate the magnetic gradients. Substantial equivalence is demonstrated also in terms of the manoeuvrability of user-controlled microrobots along a complex path

Direct laser writing of liquid crystal elastomers oriented by a horizontal electric field

Background: The ability to fabricate components capable of performing actuation in a reliable and controlled manner is one of the main research topics in the field of microelectromechanical systems (MEMS). However, the development of these technologies can be limited in many cases by 2D lithographic techniques employed in the fabrication process. Direct Laser Writing (DLW), a 3D microprinting technique based on two-photon polymerization, can offer novel solutions to prepare, both rapidly and reliably, 3D nano- and microstructures of arbitrary complexity. In addition, the use of functional materials in the printing process can result in the fabrication of smart and responsive devices. Methods: In this study, we present a novel methodology for the printing of 3D actuating microelements comprising Liquid Crystal Elastomers (LCEs) obtained by DLW. The alignment of the mesogens was performed using a static electric field (1.7 V/m) generated by indium-tin oxide (ITO) electrodes patterned directly on the printing substrates. Results: When exposed to a temperature higher than 50°C, the printed microstructures actuated rapidly and reversibly of about 8% in the direction perpendicular to the director. Conclusions: A novel methodology was developed that allows the printing of directional actuators comprising LCEs via DLW. To impart the necessary alignment of the mesogens, a static electric field was applied before the printing process by making use of flat ITO electrodes present on the printing substrates. The resulting microelements showed a reversible change in shape when heated higher than 50 °C.</p

Cavitation‐driven Deformable Microchambers Inspired by Fast Microscale Movements of Ferns

Annulus cells of fern sporangia spontaneously deform driven by water transpiration and cavitation, resulting in the peculiar macroscale catapult-like movement of the sporangium. Annulus cells' behavior, if artificially replicated, can inspire a novel class of fast actuators composed of annulus-mimicking units. However, the transpiration and cavitation-driven dynamics observed in annulus cells is never reproduced. Here, prismatic microcavities are assembled with a polydimethylsiloxane (PDMS) microfilm to realize artificial microchambers that mimic the annulus cells, replicating for the first time their evaporation-driven collapse and their fast return triggered by the nucleation of bubbles. The microchambers, in turn, can be fabricated in adjacency, resulting in bending arrays driven by transpiration. Working with an artificial system allows this study to investigate the fluidic phenomena arising from the interplay of a soft, semi-permeable membrane with a micro-confined liquid bounded by rigid walls. First, the microchambers aspect ratio influences the membrane dynamics and the bubble shape (either spherical or non-spherical). Second, the growth rate of the bubble interplay with the membrane in the expansion dynamics. This study's results demonstrate the artificial replication of annulus cells' behavior, offering a plant-like solution to realize fast, microscale movements, and a novel tool to investigate complex fluidic mechanisms involving micro-confined cavitation

3D-printed biomimetic artificial muscles using soft actuators that contract and elongate

Biomimetic machines able to integrate with natural and social environments will find ubiquitous applications, from biodiversity conservation to elderly daily care. Although artificial actuators have reached the contraction performances of muscles, the versatility and grace of the movements realized by the complex arrangements of muscles remain largely unmatched. Here, we present a class of pneumatic artificial muscles, named GeometRy-based Actuators that Contract and Elongate (GRACE). The GRACEs consist of a single-material pleated membrane and do not need any strain-limiting elements. They can contract and extend by design, as described by a mathematical model, and can be realized at different dimensional scales and with different materials and mechanical performances, enabling a wide range of lifelike movements. The GRACEs can be fabricated through low-cost additive manufacturing and even built directly within functional devices, such as a pneumatic artificial hand that is fully three-dimensionally printed in one step. This makes the prototyping and fabrication of pneumatic artificial muscle-based devices faster and more straightforward

Structured light enables biomimetic swimming and versatile locomotion of photoresponsive soft microrobots.

Microorganisms move in challenging environments by periodic changes in body shape. In contrast, current artificial microrobots cannot actively deform, exhibiting at best passive bending under external fields. Here, by taking advantage of the wireless, scalable and spatiotemporally selective capabilities that light allows, we show that soft microrobots consisting of photoactive liquid-crystal elastomers can be driven by structured monochromatic light to perform sophisticated biomimetic motions. We realize continuum yet selectively addressable artificial microswimmers that generate travelling-wave motions to self-propel without external forces or torques, as well as microrobots capable of versatile locomotion behaviours on demand. Both theoretical predictions and experimental results confirm that multiple gaits, mimicking either symplectic or antiplectic metachrony of ciliate protozoa, can be achieved with single microswimmers. The principle of using structured light can be extended to other applications that require microscale actuation with sophisticated spatiotemporal coordination for advanced microrobotic technologies.This work was in part supported by the European Research Council under the ERC Grant agreements 278213 and 291349, and the DFG as part of the project SPP 1726 (microswimmers, FI 1966/1-1). SP acknowledges support by the Max Planck ETH Center for Learning Systems.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nmat456

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes

The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This paper presents the initial results of an ongoing project whose focus is the application of Artificial Intelligence and Machine Learning techniques for conceptualizing, cleaning, and analyzing such an important and valuable dataset, with the goal of providing predictive insights to better support diabetologists in their diagnostic and therapeutic choices.Comment: The work has been presented at the conference Ital-IA 2022 (https://www.ital-ia2022.it/