This Supplementary Information includes: Section S1- Fabrication method Section S2- Actuation method Section S3- Analysis of sixth-DOF torque Section S4- Experiments Figures S1-S31 Supporting Table References Other supplementary materials for this manuscript include the following:Supporting SI Videos S1-S10 Corresponding author(s) Email: email@example.com
Magnetic miniature robots (MMRs) are mobile actuators that can exploit their size to non-invasively access highly confined, enclosed spaces. By leveraging on such unique abilities, MMRs have great prospects to transform robotics, biomedicine and materials science. As having high dexterity is critical for MMRs to enable their targeted applications, existing MMRs have developed numerous soft-bodied gaits to locomote in various environments. However, there exist two critical limitations that have severely restricted their dexterity: (i) MMRs capable of multimodal soft-bodied locomotion have only demonstrated five-degrees-of-freedom (five-DOF) motions because the sixth-DOF rotation about their net magnetic moment axis is uncontrollable; (ii) six-DOF MMRs have only realized one mode of soft-bodied, swimming locomotion. Here we propose a six-DOF MMR that can execute seven modes of soft-bodied locomotion and perform 3-dimensional pick-and-place operations. By optimizing its harmonic magnetization profile, our MMR can produce 1.41-63.9 folds larger sixth-DOF torque than existing MMRs with similar profiles, without compromising their traditional five-DOF actuation capabilities. The proposed MMR demonstrated unprecedented dexterity; it could jump through narrow slots to reach higher grounds; use precise orientation control to roll, two-anchor crawl and swim across tight openings with strict shape constraints; perform undulating crawling across three different planes in convoluted channels. Keywords: Magnetic materials; soft actuators; miniature robots; locomotion. Corresponding author(s) Email: firstname.lastname@example.org
The origami technique realizes unique mechanical properties of sheet materials without additional parts. In this study, a self-folded corrugated structure (SCS) is developed based on the reinforcing properties of the origami technique. The corrugated structures are employed as the core material for a high-strength, open-channel sandwich structure. Research on self-folded core materials is scarce; thus, a design concept is proposed, and the mechanical properties of the SCS are evaluated. First, the structural parameters of the SCS fabricated by changing the printing parameters (e.g., linewidth and number of lines/creases), to derive the structural model are determined. The model facilitates the design of an SCS with the desired structure. Thereafter, the mechanical properties of the SCSs are evaluated by conducting three-point bending tests to determine the essential design parameters corresponding to high stiffness. Moreover, SCSs can be stacked without occupying space, thus leading to improved strength. These SCSs fabricated using self-folding paper by ink-jet printing are low cost and eco-friendly. Moreover, they are specialized for rapid design and fabrication, depending on the application. This paper proposes the use of SCS as a novel smart core because it exhibits a high transportation efficiency and stiffness without additional components. Corresponding author(s) Email: email@example.com , firstname.lastname@example.org
Figure S1. a) The memristive synaptic behavior with an ideally symmetric and linear weight update ability (constant ΔG for identical pulses) but limited conductance levels (N =20). b) Test accuracy for 10,000 images in the MNIST dataset obtained during the training of memristive DBN as a function of the training epoch (CDth=64).
This Supporting Information includes:Figure S1, S2, S3Supplementary VideosVideo S1: Process of self-folding of the SCS with 4 creases.Video S2: Process of self-folding of the SCS with 10 creases. Video S3: Demonstration of stacking SCS with 10 sheets of paper. Video S4: Three-point bending test of the SCS.
Shape memory Nitinol has long been used for actuation. However, utilizing Nitinol to fabricate novel devices for various applications is a challenge, but has shown incredible promise and impacts. Bistable metal strips are widely adopted for shape morphing purposes (primarily in kid’s toys, e.g., snap bracelets) due to their easy and robust transformation between two states. In this paper, we combine Nitinol shape memory alloy and bistable metal strip to fabricate a swimming actuator with both slow moving and fast snapping capability, akin to an octopus swimming slowly in water, but quickly moving upon encountering a threat. The actuator developed here can also swim in multiple directions, all controlled by a wireless module. Furthermore, we demonstrate that an on-board sensor can be incorporated for potential environmental monitoring applications. Taken together, along with the fact that the device developed here has no mechanical parts, makes this an interesting potential alternative to more expensive, and energy consuming boats.
Memristive devices being applied in neuromorphic computing are envisioned to significantly improve the power consumption and speed of future computing platforms. The materials used to fabricate such devices will play a significant role in their viability. Graphene is a promising material, with superb electrical properties and the ability to be produced sustainably. In this paper, we demonstrate that a fabricated graphene-pentacene memristive device can be used as synapses within Spiking Neural Networks (SNNs) to realise Spike Timing Dependent Plasticity (STDP) for unsupervised learning in an efficient manner. Specifically, we verify operation of two SNN architectures tasked for single digit (0-9) classification: (i) a simple single-layer network, where inputs are presented in 5x5 pixel resolution, and (ii) a larger network capable of classifying the Modified National Institute of Standards and Technology (MNIST) dataset, where inputs are presented in 28x28 pixel resolution. Final results demonstrate that for 100 output neurons, after one training epoch, a test set accuracy of up to 86% can be achieved, which is higher than prior art using the same number of output neurons. We attribute this performance improvement to homeostatic plasticity dynamics that we used to alter the threshold of neurons during training. Our work presents the first investigation of the use of green-fabricated graphene memristive devices to perform a complex pattern classification task. This can pave the way for future research in using graphene devices with memristive capabilities in neuromorphic computing architectures. In favour of reproducible research, we make our code and data publicly available https://anonymous.4open.science/r/c69ab2e2-b672-4ebd-b266-987ee1fd65e7.
Soft fluidic actuators produce continuous and life-like motions that are intrinsically safe, but current designs are not yet mature enough to enable large deployment with high force and low-cost fabrication methods. Here, soft fluidic actuators with two superimposed origami architectures are reported. Driven by a fluid input, the presented dual-origami soft actuators produce quasi-sequential deployment and bending motion that is guided by unsymmetric unfolding of low-stretchable origami components. The dominance between the deployment and bending can be shifted by varying the unfolding behavior, enabling pre-programming of the motion. The proposed origami-inspired soft actuators are directly fabricated by low-cost fused deposition modeling 3D-printing, and subjected to a heat treatment post-processing to enhance the fluid sealing performance. Finally, soft gripper applications are presented and they successfully demonstrate gripping tasks that each requires strength, delicacy, precision and dexterity. The dual-origami approach offers a design guidance for soft robots to embody grow-and-retract motion with a small initial form factor, promising for applications in next-generation soft robotic systems.
Intelligence in its decisions is a trait that we have grown to expect from a cyber-physical system. In particular that it makes the right choices at runtime, i.e., those that allow it fulfill its tasks, even in case of faults or unexpected interactions with its environment. Analyzing how to continuously achieve the currently desired (and possibly continuously changing) goals and adapting its behavior to reach these goals is undoubtedly a serious challenge. This becomes even more challenging if the atomic actions a system can implement become unreliable due to faulty components or some exogenous event out of its control. In this paper, we propose a solution for the presented challenge. In particular, we show how to adopt a light-weight diagnosis concept to cope with such situations. The approach is based on rules coupled with means for rule selection that are based on previous information regarding the success or failure of rule executions. We furthermore present a Java-based framework of the light-weight diagnosis concept, and discuss the results obtained from an experimental evaluation considering several application scenarios. At the end, we present a qualitative comparison with other related approaches that should help the reader decide which approach works best for them.
Engineering microscopic collectives of cells or microrobots is challenging due to the often-limited capabilities of the individual agents, our inability to reliably program their motion and local interactions, and difficulties visualising their behaviours. Here, we present a low-cost, modular and open-source Dynamic Optical MicroEnvironment (DOME) and demonstrate its ability to augment microagent capabilities and control collective behaviours using light. The DOME offers an accessible means to study complex multicellular phenomena and implement de-novo microswarms with desired functionalities. Corresponding author(s) Email: email@example.com firstname.lastname@example.org
Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of isolated leukocytes acquired from a custom-built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, we not only achieved high accuracy in differentiating B and T lymphocytes, but also classified CD4 and CD8 cells, therefore outperforming the classification accuracy of most current hematology analyzers. We validated the performance of AIRFIHA in a randomly selected test set and cross-validated it across all blood donors. Owing to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases. Corresponding author(s) Email: email@example.com, firstname.lastname@example.org
Figure S1. Detailed explanation of microfabrication step of fully integrated NIR-LFC. a) The wafer-level microfabrication of iMLA-AFF involves a thin Cr lift-off, and plasma enhanced chemical vapor deposition (PECVD) of SiO2, and a thick Cr lift-off (6 nm Cr – 135 nm SiO2 – 130 nm Cr), photolithographic patterning of DNR photoresist (DNR L300-D1, Dong-jin Semichem, Co., Ltd, Korea), and thermal reflow. Note that a DNR photoresist exhibits both UV curable (Negative photoresist) and thermoplastic characteristic, suitable for metal lift-off as well as microlens formation. The hydrophobic coating of fluorocarbon (C4F8) effectively prevents the lateral expansion of microlenses on a metal surface during thermal reflow. iMLA-AFF are inversely bonded to an image sensor with a 60 μm gap spacer and packaged to a compact objective lens by using a UV curable adhesive. The NIR-LFC is fully assembled by combining a 8.5 mm × 4.7 mm printed circuit board with two VCSEL sources and VCSEL housing. b) A scanning electron microscope (SEM) of hexagonally arranged iMLA-AFF with 30 μm in microlens diameter and 3 μm in microlens gap. c) A photograph of fully packaged NIR-LFC. The camera module is connected to flexible extension cable and delivers raw image to Raspberry Pi 4(B). The total physical dimension of camera module is 8.5 mm × 14.0 mm × 5.6 mm.
Soft actuators and robotic devices have been increasingly applied to the field of rehabilitation and assistance, where safe human and machine interaction is of particular importance. Compared with their widely used rigid counterparts, soft actuators and robotic devices can provide a range of significant advantages; these include safe interaction, a range of complex motions, ease of fabrication and resilience to a variety of environments. In recent decades, significant effort has been invested in the development of soft rehabilitation and assistive devices for improving a range of medical treatments and quality of life. This review provides an overview of the current state-of-the-art in soft actuators and robotic devices for rehabilitation and assistance, in particular systems that achieve actuation by pneumatic and hydraulic fluid-power, electrical motors, chemical reactions and soft active materials such as dielectric elastomers, shape memory alloys, magnetoactive elastomers, liquid crystal elastomers and piezoelectric materials. Current research on soft rehabilitation and assistive devices is in its infancy, and new device designs and control strategies for improved performance and safe human-machine interaction are identified as particularly untapped areas of research. Finally, insights into future research directions are outlined.Corresponding author(s) Email: email@example.com, firstname.lastname@example.org
Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: email@example.com
Scene flow tracks the three-dimensional (3D) motion of each point in adjacent point clouds. It provides fundamental 3D motion perception for autonomous driving and server robot. Although the Red Green Blue Depth (RGBD) camera or Light Detection and Ranging (LiDAR) capture discrete 3D points in space, the objects and motions usually are continuous in the macro world. That is, the objects keep themselves consistent as they flow from the current frame to the next frame. Based on this insight, the Generative Adversarial Networks (GAN) is utilized to self-learn 3D scene flow with no need for ground truth. The fake point cloud of the second frame is synthesized from the predicted scene flow and the point cloud of the first frame. The adversarial training of the generator and discriminator is realized through synthesizing indistinguishable fake point cloud and discriminating the real point cloud and the synthesized fake point cloud. The experiments on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) scene flow dataset show that our method realizes promising results without ground truth. Just as human, the proposed method can identify the similar local structures of two adjacent frames even without knowing the ground truth scene flow. Then, the local correspondence can be correctly estimated, and further the scene flow is correctly estimated. Corresponding author(s) Email: firstname.lastname@example.org
Single-use jumping robots that are mass-producible and biodegradable could be quickly released for environmental sensing applications. Such robots would be pre-loaded to perform a set number of jumps, in random directions and with random distances, removing the need for onboard energy and computation. Stochastic jumpers build on embodied randomness and large-scale deployments to perform useful work. This paper introduces simulation results showing how to construct a large group of stochastic jumpers to perform environmental sensing, and the first demonstration of robot prototypes that can perform a set number of sequential jumps, have full-body sensing, and are well suited to be made biodegradable. Corresponding author(s) Email: email@example.com firstname.lastname@example.org
Authors Xulei Wu1,2, Bingjie Dang2, Hong Wang5, Xiulong Wu1,*, and Yuchao Yang2,3,4,* 1School of Electronics and Information Engineering, Anhui University, Hefei 230601, China. 2Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing 100871, China.
Bacteria-mediated drug delivery systems comprising nanotherapeutics conjugated onto bacteria synergistically augment the efficacy of both therapeutic modalities in cancer therapy. Nanocarriers preserve therapeutics' bioavailability and reduce systemic toxicity, while bacteria selectively colonize the cancerous tissue, impart intrinsic and immune-mediated antitumor effects, and propel nanotherapeutics interstitially. The optimal bacteria-nanoparticle (NP) conjugates would carry the maximal NP load with minimal motility speed hindrance for effective interstitial distribution. Furthermore, a well-defined and repeatable NP attachment density distribution is crucial to determining these biohybrid systems' efficacious dosage and robust performance. Herein, we utilized our Nanoscale Bacteria-Enabled Autonomous Delivery System (NanoBEADS) platform to investigate the effects of assembly process parameters of mixing method, volume, and duration on NP attachment density and repeatability. We also evaluated the effect of linkage chemistry and NP size on NP attachment density, viability, growth rate, and motility of NanoBEADS. We show that the linkage chemistry impacts NP attachment density while the self-assembly process parameters affect the repeatability and, to a lesser extent, attachment density. Lastly, the attachment density affects NanoBEADS' growth rate and motility in an NP size-dependent manner. These findings will contribute to the development of scalable and repeatable bacteria-nanoparticle biohybrids for applications in drug delivery and beyond. Corresponding author(s) Email: email@example.com