Cyborg and bionic systems (Washington, D.C.)最新文献

Biohybrid robots integrate biological components with synthetic materials to harness the unique capabilities of living systems for robotic functions. This study focuses on leveraging yeast fermentation dynamics to enable actuation and sensing in soft robotic systems. By leveraging yeast's natural ability to produce carbon dioxide and generate pressure during fermentation, we demonstrate the feasibility of creating biohybrid robots with lifelike behavior and adaptability. Our research integrates bioimpedance sensing into track yeast behavior and metabolic dynamics in real time. We developed an adjustable single-resistor oscillator circuit by using a digital potentiometer to measure impedance frequency and model the yeast growth rate. Experimental results reveal the sensitivity of the single-resistor oscillator circuit to variations in yeast concentration and demonstrate the correlation between yeast behavior and actuation power. Furthermore, we highlight the potential of yeast-driven robots for various applications by demonstrating a yeast-driven soft limb capable of rotating 140° tested at different temperatures, an inflatable membrane actuator functioning as a tactile sensor detecting forces up to 4.5 N, a palpation probe for differentiating tissue stiffness, and a gripper capable of manipulating objects. This work lays the foundation for advancing biohybrid robotics by integrating yeast fermentation dynamics with bioimpedance sensing, enhancing the functionality of robotic systems.

The look-and-step behavior of biped robots requires quickly extracting planar regions and obstacles with limited computing resources. To this end, this paper proposes an efficient method representing the environment as a hybrid of feasible planar regions and a heightmap. The feasible planar regions are used for footstep planning, preventing the body from hitting obstacles, and the heightmap is used to calculate foot trajectory to avoid foot collision during the swing process. The planar regions are efficiently extracted by leveraging the organized structure of points for nearest neighbor searches. To ensure safe locomotion, these extracted planar regions exclude areas that could cause the robot's body to collide with the environment. The proposed method completes this perception process in 0.16 s per frame using only a central processing unit, making it suitable for look-and-step behavior of biped robots. Experiments conducted in typical artificial scenarios with BHR-7P and BHR-8P demonstrate its efficiency and safety, validating its effectiveness for the look-and-step behavior of biped robots.

Hip exoskeleton can provide assistance to users to augment movements in different scenarios. The assistive control for hip exoskeleton involves the interactions among exoskeleton, user, and environment, which depends on the environment perception (to predict locomotion) to design control strategy combined with gait mode and so on. Current exoskeleton control still needs to be improved in adaptation to continuous locomotion mode and different users. To address this problem, we have employed a learning-free (i.e., non-data-driven) environment perception method to improve hip exoskeleton adaptive control toward continuous locomotion mode. The adaptive control experiments were conducted on level ground and stairs on 7 subjects. The prediction accuracy for steady locomotion mode was more than 95% for each subject (ranged from 95.7% to 99.7%). The prediction accuracy for each locomotion mode transition ranged from 87.5% to 100%, and the transition timing could be detected before the end of transition period. Compared with learning-based (data-driven) approaches, our method achieves better performances in adaptive control for hip exoskeleton and shows some generalization for subjects.

In recent years, the rapid progression of artificial intelligence and the Internet of Things has led to a significant increase in the demand for advanced computing capabilities and more robust data storage solutions. In light of these challenges, neuromorphic computing, inspired by human brain's architecture and operation principle, has surfaced as a promising answer to the growing technological demands. This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. Two-dimensional (2D) materials, distinguished by their atomic thickness and tunable physical properties, exhibit substantial potential in emulating synaptic plasticity and find broad applications in neuromorphic computing. With respect to device architecture, memory devices based on floating-gate (FG) structures demonstrate robust data retention capabilities and have been widely used in the realm of flash memory. This review begins with a succinct introduction to 2D materials and FG transistors, followed by an in-depth discussion on remarkable research progress in the integration of 2D materials with FG transistors for applications in neuromorphic computing and memory. This paper offers a thorough review of the existing research landscape, encapsulating the notable progress in swiftly expanding field. In conclusion, it addresses the constraints encountered by FG transistors using 2D materials and delineates potential future trajectories for investigation and innovation within this area.

Locating tumors during laparoscopic surgery for early gastric cancers poses an important challenge because they lack involvement with the serosal layer and remain invisible within the peritoneal cavity. To address this issue, various techniques such as preoperative dye injection and magnetic clip detection systems have been introduced to aid in intraoperative tumor localization. However, these existing techniques are often intricate and lack intuition and endurance. In this study, we propose a novel approach utilizing fluorescent soft robots to accurately locate tumors within the stomach. The methodology involved placing a metal clip at the tumor site, followed by administering several soft robots labeled with Cy5. These soft robots were designed to autonomously converge around the metal clip. To validate their efficacy, we conducted animal experiments by implanting clips into the stomachs of rats and subsequently administering capsules containing the soft robots. By detecting the resulting fluorescence, we successfully identified the location of the clips within the stomach. Our findings indicate that these soft robots hold great promise as a viable alternative for localizing gastric lesions during laparoscopic surgery, which has better persistence and intuitiveness than other markup methods. Their implementation could significantly enhance the accuracy and efficiency of tumor identification in a technologically advanced and clinically accessible manner.

Recently, vision-based tactile sensors (VBTSs) have gained popularity in robotics systems. The sensing mechanisms of most VBTSs can be categorized based on the type of tactile features they capture. Each category requires specific structural designs to convert physical contact into optical information. The complex architectures of VBTSs pose challenges for traditional manufacturing techniques in terms of design flexibility, cost-effectiveness, and quality stability. Previous research has shown that monolithic manufacturing using multimaterial 3-dimensional printing technology can address these challenges but fails to bridge the gap between the design phase and creation phase of VBTSs. Thereby, in this study, we introduce the CrystalTac family, a series of VBTSs designed with on-demand sensing mechanisms and fabricated through rapid monolithic manufacturing. Case studies on the CrystalTac family demonstrate their efficiency in targeted tasks involving tactile perception, along with impressive cost-effectiveness and design flexibility. The CrystalTac family aims to highlight the potential of rapid monolithic manufacturing techniques in VBTS development and inspire further research in tactile sensing and manipulation.

High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.

Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective therapy for Parkinson's disease (PD). However, the therapeutic mechanisms remain incompletely understood, particularly regarding the extracellular space (ECS), a critical microenvironment where molecular diffusion and interstitial fluid (ISF) dynamics are essential for neural function. This study aims to explore the regulatory mechanisms of the ECS in the substantia nigra (SN) of PD rats following STN-DBS. To evaluate whether STN-DBS can modulate ECS diffusion and drainage, we conducted quantitative measurements using a tracer-based magnetic resonance imaging. Our findings indicated that, compared to the PD group, STN-DBS treatment resulted in a decreased diffusion coefficient (D*), shorted half-life (T _1/2), and increased clearance coefficient (k') in the SN. To investigate the mechanisms underlying these changes in molecular diffusion, we employed enzyme-linked immunosorbent assay (ELISA), Western blotting (WB), and microdialysis techniques. The results revealed that STN-DBS led to an increase in hyaluronic acid content, elevated expression of excitatory amino acid transporter 2 (EAAT2), and a reduction in extracellular glutamate concentration. Additionally, to further elucidate the mechanisms influencing ISF drainage, we employed immunofluorescence and immunohistochemical techniques for staining aquaporin-4 (AQP-4) and α-synuclein. The results demonstrated that STN-DBS restored the expression of AQP-4 while decreasing the expression of α-synuclein. In conclusion, our findings suggest that STN-DBS improves PD symptoms by modifying the ECS and enhancing ISF drainage in the SN regions. These results offer new insights into the mechanisms and long-term outcomes of DBS in ECS, paving the way for precision therapies.

Recognizing hand gestures from neural control signals is essential for natural human-machine interaction, which is extensively applied to prosthesis control and rehabilitation. However, establishing associations between the neural control signals of motor units and gestures remains an open question. Here, we propose a channel-wise cumulative spike train (cw-CST) image-driven model (cwCST-CNN) for hand gesture recognition, leveraging the spatial activation patterns of motor unit firings to distinguish motor intentions. Specifically, the cw-CSTs of motor units were decomposed from high-density surface electromyography using a spatial spike detection algorithm and were further reconstructed into images according to their spatial recording positions. Then, the resultant cwCST-images were fed into a customized convolutional neural network to recognize gestures. Additionally, we conducted an experiment involving 10 gestures and 10 subjects and compared the proposed method with 2 root-mean-square (RMS)-based approaches and a cw-CST-based approach, namely, RMS-image-driven convolutional neural network classification model, RMS feature with linear discrimination analysis classifier, and cw-CST discharge rate feature with linear discrimination analysis classifier. The results demonstrated that cwCST-CNN outperformed the other 3 methods with a higher classification accuracy of 96.92% ± 1.77%. Moreover, analysis of cw-CST and RMS features showed that the former had better separability across gestures and consistency considering training and testing datasets. This study provides a new solution and enhances the accuracy of gesture recognition using neural drive signals in human-machine interaction.

Quadruped manipulators can use legs to mimic legged animals for crossing unstructured environments. They can also use a bionic arm to execute manipulation tasks. The increasing demands for such robots have pushed research progress. However, there remain challenging works in their usage of a high degree of freedom. To solve this redundant problem, we propose a novel motion coordination framework based on multi-task prioritization and null-space projection. The framework can adaptively generate optimal motion for different parts of the robot considering 3 prioritized tasks. The tasks include end-effector trajectory tracking, motion redistribution to meet physical constraints, and manipulability enhancement. The motion is then executed by a whole-body controller incorporating dynamics, inverse kinematics, multiobjective priorities, and force constraints. Experiments both in simulation and on the robot platform validate the advantages and effectiveness of the algorithm. The robot can finish robust and accurate operational space end-effector tracking with errors less than 3 cm.