Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.
Multi-chamber soft pneumatic actuators (m-SPAs) have been widely used in soft robotic systems to achieve versatile grasping and locomotion. However, existing m-SPAs have slow actuation speed and are either limited by a finite air supply or require energy-consuming hardware to continuously supply compressed air. Here, we address these shortcomings by introducing an internal exhaust air recirculation (IEAR) mechanism for high-speed and low-energy actuation of m-SPAs. This mechanism recirculates the exhaust compressed air and recovers the energy by harnessing the rhythmic actuation of multiple chambers. We develop a theoretical model to guide the analysis of the IEAR mechanism, which agrees well with the experimental results. Comparative experimental results of several sets of m-SPAs show that our IEAR mechanism significantly improves the actuation speed by more than 82.4% and reduces the energy consumption per cycle by more than 47.7% under typical conditions. We further demonstrate the promising applications of the IEAR mechanism in various pneumatic soft machines and robots such as a robotic fin, fabric-based finger, and quadruped robot. Corresponding author(s) Email: [email protected]
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 protected]
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 protected] [email protected]
The interest in soft pneumatic actuators has been growing rapidly in robotics, owing to the contact adaptability with the material softness. However, these actuators are mostly controlled by rigid electronic pneumatic valves, which can hardly be integrated into the robot itself, limiting its mobility and adaptability. Recent advances in soft or electronics-free valve designs provide the potential to achieve an integrated soft robotic system with reduced weight and rigidity. Nevertheless, the challenge in valve response remains open. To enable dynamic control of a soft pneumatic actuator, a fast-response proportional valve is needed. In this paper, we explored the potential of Ecoflex-based magnetorheological elastomer (MRE) membrane to create a proportional valve that can be used in the control of a soft robot made from the same silicone material. Experimental characterization shows that the proposed MRE valve (30 mm \(\times\) 30 mm \(\times\) 15 mm, 30 grams) can hold pressure up to 41.3 kPa and regulate the airflow in an analog manner. The valve is used to perform closed-loop Proportional-Integral-Differential (PID) control with 50 Hz on a soft pneumatic actuator and is able to control the pressure within the actuator chamber with a root-mean-square error of 0.05 kPa. Corresponding author(s) Email: [email protected]
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment, for which many reports conclude the advantageous use of convolutional neural networks (CNNs) in prostate lesion detection and classification (PLDC). However, the network training inevitably involves prostate magnetic resonance (MR) images from multiple sites/cohorts. There always exists variation in scanning protocol among the cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Recent domain adaptation (DA) models can alleviate the inherent cross-site heterogeneity. Some could leverage cross-domain knowledge transfer using whole-slide images, without prior knowledge of lesion regions. In this paper, we propose a coarse mask-guided deep domain adaptation network (CMD²A-Net) in order to develop a fully automated framework for PLDC using multi-cohort images. No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion-related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. As a result, the features of both prostate lesion and region can be fused to align the robust features between the source and target domains. Experiments have been performed on 512 mpMRI sets from datasets of PROSTATEx (with 330 sets) and two cohorts, A (with 74 sets) and B (with 108 sets). Using the ensemble learning, our CMD²A-Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large-scale public dataset PROSTATEx to our small-scale target domains. Our results and ablation study also support the CMD²A-Net’s effectiveness in lesion classification between benign or malignant, compared to the state-of-the-art models Corresponding author(s) Email: [email protected] (K.W. Kwok), [email protected] (V. Vardhanabhuti)
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 protected], [email protected]
Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco-friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with bio-realistic synaptic plasticity for energy efficient learning processes is still challenging. Here, we demonstrate a biodegradable and flexible polymer based memristor, suitable for the spike-dependent learning process. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike-dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco-friendly wearable intelligent systems.Corresponding author(s) Email: [email protected]
Excavation of regolith is the enabling process for many of the in-situ resource utilization (ISRU) efforts that are being considered to aid in the human exploration of the moon and Mars. Most proposed planetary excavation systems are integrated with a wheeled vehicle, but none yet have used a screw-propelled vehicle which can significantly enhance the excavation performance. Therefore, CASPER, a novel screw-propelled excavation rover is developed and analyzed to determine its effectiveness as a planetary excavator. The excavation rate, power, velocity, cost of transport, and a new parameter, excavation transport rate, are analyzed for various configurations of the vehicle through mobility and excavation tests performed in silica sand. The optimal configuration yielded a 30 kg/hr excavation rate and 10.2 m/min traverse rate with an overall system mass of 3.4 kg and power draw of less than 30 W. These results indicate that this architecture shows promise as a planetary excavation because it provides significant excavation capability with low mass and power requirements. Corresponding author(s) Email: [email protected]
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.
Deformability and self-adaptability are important for soft robots in order to deal with uncertain and varying situations and environments during movement and navigation. Droplet-based robots are great candidates to travel inside narrow and constrained spaces without damaging the interfaces due to their extreme deformability and liquid nature, which enables smooth contact between robots and target spaces. Here, we propose magnetic liquid metal droplet robots, comprising liquid metal and carbonyl iron, that can perform reversible telescopic deformation, bending, and on-demand locomotion. The magnetic liquid metal-based robots can perform on demand and reversible coalescence and splitting by intricately applying magnetic fields. Importantly, the liquid metal robot can perform phase transition to fix the desired shape after the programmable shape encoding. The liquid metal-based soft robots can serve as dynamic and recyclable switches for complex circuits, and are capable of repairing damaged sections of microcircuits by remote actuation, controllable coalescence, and on-demand circuit welding. The technology provides a new application scenario of droplet-based soft robots for on-demand circuit welding and transient recyclable electronics. Corresponding author(s) Email: X.Z.: [email protected]; L.Z.: [email protected]; B.W.: [email protected]
With advancements in automation and high-throughput techniques, complex materials discovery with multiple conflicting objectives can now be tackled in experimental labs. Given that physical experimentation is greatly limited by evaluation budget, maximizing efficiency of optimization becomes crucial. We discuss the limitations of using hypervolume as a performance indicator for desired optimality across the entire multi-objective optimization run and propose new metrics specific to experimentation: ability to perform well for complex high-dimensional problems, minimizing wastage of evaluations, consistency/robustness of optimization, and ability to scale well to high throughputs. With these metrics, we perform a comparison of two conceptually different and state-of-the-art algorithms (Bayesian and Evolutionary) on synthetic and real-world datasets. We discuss the merits of both approaches with respect to exploration and exploitation, where fully resolving the Pareto Front could be the main aim for greater scientific value in understanding materials space, and thus provide a perspective for materials scientists to implement optimization in their platforms.
Developing on-site biomarker enrichment platforms could help to improve the diagnosis of gastrointestinal (GI) tract diseases at early stages. Medical procedures, such as colonoscopies and imaging techniques, are used to diagnose disease, but are not easily accessible for repeat measurements. In the other hand, liquid biopsies, e.g., blood, urine, or fecal samples, have become important sampling strategies to identify health concerns. Herein, a robotic pill is designed for collecting relevant biomarkers from the GI over prolonged sampling periods. The robotic pill comprises a magnetic core for locomotion, a delayed gate mechanism that controls sampling location based on changes in its environment, and an enrichment module that traps biomarkers in an absorbent matrix while enabling biofluid to pass through the chamber. The robotic pill was assessed to sample microparticles, proteins, and bacteria from solution. Moreover, the robotic pill was capable of directed locomotion in complex environments and docking in a targeted region against fluid flow. Utilization of an untethered robotic sampling system could provide a tool to investigate aspects of disease initiation and progression for early diagnosis and therapy monitoring.
This Supporting information includes:1. Component Selection and Performance of SFA2. Actuator Manufacturing and Preparation of Conductive Ink 3. Average Thickness of Conductive Coating layer on PU Foam4. Time Response of the Actuator in Different Modes 5. Characterization and Experimental Setup6. Measurement and Data Analysis7. Design Specifications of Soft Robotic Applications8. Supporting Video Corresponding author Email: [email protected], [email protected]
Aerial robots can autonomously collect temporal and spatial high-resolution environmental data. This data can then be utilized to develop mathematical ecology models to understand the impact of climate change on our habitat. In case of the drone's malfunction the incorporated materials can threaten vulnerable environments. The recent introduction of transient robotics has enabled the development of biodegradable, environmental sensing drones capable of degrading in their environment. However, manufacturing methods for environmental sensing transient drones are rarely discussed. In this work, we highlight a manufacturing framework and material selection process featuring biopolymer-based, high-strength composite cryogels and printed carbon-based electronics for transient drones. We found that gelatin and cellulose based cryogels mechanically outperform other biopolymer composites while having a homogeneous micro-structure and high stiffness-to-weight ratio. The selected materials are used to manufacture a flying-wing air-frame, while the incorporated sensing skin is capable of measuring the elevons' deflection angles as well as ambient temperature. Our results demonstrate how gelatin-cellulose cryogels can be used to manufacture lightweight transient drones while printing carbon conductive electronics is a viable method for designing sustainable, integrated sensors. The proposed methods can be used to guide the development of lightweight and rapidly degrading robots, featuring eco-friendly sensing capabilities.Corresponding author(s) Email: [email protected], [email protected]
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.
Conventional vision systems suffer from lots of data handling between memory and processing units. Inspired by how humans recognize noisy images and the flexible modulation on the timescale of ion dynamics inside an emerging memtransistor, we report a novel neuromorphic vision system based on the ion-modulated memtransistors. By controlling the ion doping processes under adequate stimuli strengths, both short-term and long-term ion dynamics can be utilized to deliver energy-efficient data processing. When dealing with image reconstructions, the short-term accumulation effect of the device can help filter noises in a set of received noisy images while enhancing the original pattern information. The increased contrast can help distinguish the actual contents. To demonstrate systematic performances with the reconfiguration of devices, we extract the nonlinear relationship between channel conductance variation and the amplitude of gate pulses into the network-level simulation. Also, with the nonvolatile conductance change characteristic, the task of recognizing noisy images is performed to verify the versatility of ion-modulated memtransistors in the neuromorphic artificial vision systems.
A prominent problem in computer vision is occlusion, which occurs when an object’s key features temporarily disappear behind another crossing body, causing the computer to struggle with image detection. While the human brain is capable of compensating for the invisible parts of the blocked object, computers lack such scene interpretation skills. Cloud computing using convolutional neural networks is typically the method of choice for handling such a scenario. However, for mobile applications where energy consumption and computational costs are critical, cloud computing should be minimized. In this regard, we propose a computer vision sensor capable of efficiently detecting and tracking covered objects without heavy reliance on occlusion handling software. Our edge-computing sensor accomplishes this task by self-learning the object prior to the moment of occlusion and uses this information to “reconstruct” the blocked invisible features. Furthermore, the sensor is capable of tracking a moving object by predicting the path it will most likely take while travelling out of sight behind an obstructing body. Finally, sensor operation is demonstrated by exposing the device to various simulated occlusion events. Keywords: Computer vision, occlusion handling, edge computing, object tracking, dye sensitized solar cell. Corresponding author Email: [email protected]