Making Nanofiber Membrane Stand on End to Construct Vertically Interfacial Evaporators for Efficient Solar Evaporation, Omnidirectional Solar Absorption, and Ultrahigh-Salinity Brine Desalination.

Solar-driven interfacial desalination is widely considered to be a promising technology to address the global water crisis. This study proposes a novel electrospun nanofiber-based all-in-one vertically interfacial solar evaporator endowed with a high steam generation rate, steady omnidirectional evaporation, and enduring ultrahigh-salinity brine desalination. In particular, the electrospun nanofiber is collected into the tubular structure, followed by spraying with a dense crosslinked poly(vinyl alcohol) film, which renders them sufficiently strong for the preparation of a vertically array evaporator. The integrated evaporator made an individual capillary as a unit to form multiple thermal localization interfaces and steam dissipation channels, realizing zone heating of water. Thus a high steam generation rate exceeding 4.0 kg m-2 h-1 in pure water is demonstrated even under omnidirectional sunlight, and outperforms existing evaporators. Moreover, salt ions in the photothermal layer can be effectively transported to the water in capillaries and subsequently exchanged with the bulk water due to the strong action of capillary force, which ensures an ultrahigh desalination rate (≈12.5 kg m-2 h-1 under 3 sun) in 25 wt% concentration brine over 300 min. As such, this work provides a meaningful roadmap for the development of state-of-the-art solar-driven interfacial desalination.

A Rolling-Bead Triboelectric Nanogenerator for Harvesting Omnidirectional Wind-Induced Energy toward Shelter Forests Monitoring.

Shelter forests (or shelter-belts), while crucial for climate regulation, lack monitoring systems, e.g., Internet of Things (IoT) devices, but their abundant wind energy can potentially power these devices using the trees as mounting points. To harness wind energy, an omnidirectional fluid-induced vibration triboelectric nanogenerator (OFIV-TENG) has been developed. The device is installed on shelter forest trees to harvest wind energy from all directions, employing a fluid-induced vibration (FIV) mechanism (fluid-responding structure) that can capture and use wind energy, ranging from low wind speeds (vortex vibration) to high wind speeds (galloping). The rolling-bead triboelectric nanogenerator (TENG) can efficiently harvest energy while minimizing wear and tear. Additionally, the usage of double electrodes results in an effective surface charge density of 21.4 µC m-2 , which is the highest among all reported rolling-bead TENGs. The collected energy is utilized for temperature and humidity monitoring, providing feedback on the effect of climate regulation in shelter forests, alarming forest fires, and wireless wind speed warning. In general, this work provides a promising and rational strategy, using natural resources like trees as the supporting structures, and shows broad application prospects in efficient energy collection, wind speed warning, and environmentally friendliness.

High-Performance Flexible Broadband Photoelectrochemical Photodetector Based on Molybdenum Telluride.

Flexible broadband photodetectors are desired but challenging to be fabricated for next-generation wearable intelligent optoelectronic devices. Considering the narrow bandgap and strong light absorption, molybdenum telluride (MoTe2) based photoelectrochemical photodetectors are successfully assembled by liquid phase exfoliation accompanied with the electrophoretic deposited method. This MoTe2-based photodetector shows a broadband detection in ultraviolet-near-infrared band, long-term stability within 18000 s, and fast response in millisecond-level (response time≈19 ms, recovery time≈26 ms). More importantly, even though the MoTe2 photodetector is bent and twisted at a high degree for several hundred times, it still shows excellent flexibility with stable on-off switching characteristics. Additionally, this photodetector displays a good response for rotation angles in the range from 0° to 360°, and the extracted Iph maintain almost the same value approximately 0.97 µA cm-2, suggesting an omnidirectional detection capability. This work demonstrates the proposed flexible photoanode shows a great potential in future broadband omnidirectional detection systems.

Ag-modified enhance the performances of ZnO@CFs based omnidirectional photoelectrochemical ultraviolet detectors.

There are several prospective applications for omnidirectional ultraviolet (UV) detectors and underwater detection detectors in optical systems and optical fields. In this work, ZnO nanorods arrays were grown on carbon fibers (CFs). An appropriate amount of Ag nanoparticles (NPs) was deposited on the surface of ZnO nanorods by photochemical deposition. This improved the performance of photoelectrochemical (PEC) based UV detectors. Under 365 nm and 10 mW cm-2UV irradiation, the photocurrent density of the 30s-Ag/ZnO@CFs based PEC UV detector can reach 1.28 mA cm-2, which is about 7 times that of the ZnO@CFs based PEC UV detector, and the rising time is shortened from 0.17 to 0.10 s. The reason is that increased absorption of ultraviolet light induced by the localized surface plasmon resonance. In addition, the detector exhibits a good flexibility and remains flexible after hundreds of bends and twists. Moreover, the detector is responsive in the range of rotation angle from 0° to 360°. It provides an insight to improve the photoelectric performance and underwater omnidirectional detection ability of the PEC UV detector.

Metabolic cost and mechanical work of walking in a virtual reality emulator.

PURPOSE: The purpose of this study was to investigate the metabolic cost (C), mechanical work, and kinematics of walking on a multidirectional treadmill designed for locomotion in virtual reality. METHODS: Ten participants (5 females, body mass 67.2 ± 8.1 kg, height 1.71 ± 0.07 m, age 23.6 ± 1.9 years, mean ± SD) walked on a Virtuix Omni multidirectional treadmill at four imposed stride frequencies: 0.70, 0.85, 1.00, and 1.15 Hz. A portable metabolic system measured oxygen uptake, enabling calculation of C and the metabolic equivalent of task (MET). Gait kinematics and external, internal, and total mechanical work (WTOT) were calculated by an optoelectronic system. Efficiency was calculated either as WTOT/C or by summing WTOT to the work against sliding frictions. Results were compared with normal walking, running, and skipping. RESULTS: C was higher for walking on the multidirectional treadmill than for normal walking, running, and skipping, and decreased with speed (best-fit equation: C = 20.2-27.5·speed + 15.8·speed2); the average MET was 4.6 ± 1.4. Mechanical work was higher at lower speeds, but similar to that of normal walking at higher speeds, with lower pendular energy recovery and efficiency; differences in efficiency were explained by the additional work against sliding frictions. At paired speeds, participants showed a more forward-leaned trunk and higher ankle dorsiflexion, stride frequency, and duty factor than normal walking. CONCLUSION: Walking on a multidirectional treadmill requires a higher metabolic cost and different mechanical work and kinematics than normal walking. This raises questions on its use for gait rehabilitation but highlights its potential for high-intensity exercise and physical activity promotion.

Stretchable origami robotic arm with omnidirectional bending and twisting.

Inspired by the embodied intelligence observed in octopus arms, we introduce magnetically controlled origami robotic arms based on Kresling patterns for multimodal deformations, including stretching, folding, omnidirectional bending, and twisting. The highly integrated motion of the robotic arms is attributed to inherent features of the reconfigurable Kresling unit, whose controllable bistable deploying/folding and omnidirectional bending are achieved through precise magnetic actuation. We investigate single- and multiple-unit robotic systems, the latter exhibiting higher biomimetic resemblance to octopus' arms. We start from the single Kresling unit to delineate the working mechanism of the magnetic actuation for deploying/folding and bending. The two-unit Kresling assembly demonstrates the basic integrated motion that combines omnidirectional bending with deploying. The four-unit Kresling assembly constitutes a robotic arm with a larger omnidirectional bending angle and stretchability. With the foundation of the basic integrated motion, scalability of Kresling assemblies is demonstrated through distributed magnetic actuation of double-digit number of units, which enables robotic arms with sophisticated motions, such as continuous stretching and contracting, reconfigurable bending, and multiaxis twisting. Such complex motions allow for functions mimicking octopus arms that grasp and manipulate objects. The Kresling robotic arm with noncontact actuation provides a distinctive mechanism for applications that require synergistic robotic motions for navigation, sensing, and interaction with objects in environments with limited or constrained access. Based on small-scale Kresling robotic arms, miniaturized medical devices, such as tubes and catheters, can be developed in conjunction with endoscopy, intubation, and catheterization procedures using functionalities of object manipulation and motion under remote control.

Gait patterns during overground and virtual omnidirectional treadmill walking.

BACKGROUND: Omnidirectional treadmills (ODTs) offer a promising solution to the virtual reality (VR) locomotion problem, which describes the mismatch between visual and somatosensory information and contributes to VR sickness. However, little is known about how walking on ODTs impacts the biomechanics of gait. This project aimed to compare overground and ODT walking and turning in healthy young adults. METHODS: Fifteen young adults completed forward walk, 180° turn, and 360° turn tasks under three conditions: (1) overground, (2) on the Infinadeck ODT in a virtual environment without a handrail, and (3) on the ODT with a handrail. Kinematic data for all walking trials were gathered using 3D optical motion capture. RESULTS: Overall, gait speed was slower during ODT walking than overground. When controlling for gait speed, ODT walking resulted in shorter steps and greater variability in step length. There were no significant differences in other spatiotemporal metrics between ODT and overground walking. Turning on the ODT required more steps and slower rotational speeds than overground turns. The addition of the stability handrail to the ODT resulted in decreased gait variability relative to the ODT gait without the handrail. CONCLUSION: Walking on an ODT resembles natural gait patterns apart from slower gait speed and shorter step length. Slower walking and shorter step length are likely due to the novelty of physically navigating a virtual environment which may result in a more conservative approach to gait. Future work will evaluate how older adults and those with neurological disease respond to ODT walking.

A Radiation-Pattern Reconfigurable Antenna Array for Vehicular Communications.

This paper presents a low-profile reconfigurable antenna array capable of five radiation-pattern modes for vehicular communication applications. The antenna array consists of four antenna elements, each containing four square patches. Exciting one of the square patches generates a broadside radiation. A square parasitic patch is added at the rear of the excited patch, and two square parasitic patches are placed at the front. By optimizing the design of these parasitic patches, including the treatment of center slotting and addition of shorting pins, the antenna element achieves an end-fire beam with a certain tilt angle. On this basis, a reconfigurable feeding network is designed with 1:1 and 1:4 output modes. By connecting the reconfigurable feeding network to the four antenna elements and altering the on/off states of the PIN diodes in the feeding network, a reconfigurable antenna with four end-fire beams and one omnidirectional beam in its radiation pattern is realized. Measurement results demonstrate an excellent impedance bandwidth, radiation pattern, and gain performance in all modes. The four end-fire and one omnidirectional radiation characteristics make it highly suitable for vehicular communication applications.

Omnidirectional Sensor Design for Distributed Laser Measurement Systems.

Distributed laser measurement systems, widely used in high-end equipment such as airplanes, ships, and other manufacturing fields, face challenges in large spatial measurements due to laser plane obstructions and weak intersections. This paper introduces a novel omnidirectional sensor with enhanced adaptability to complex environments and improved measurement accuracy. Initially, an integrated omnidirectional measurement model is established, followed by the analysis of the optical path of the front-end detector, and the design of a signal-conditioning circuit for the photoelectric conversion of the front-end laser signal, Subsequently, a circuit testing platform is established to validate the detection functionality, and the corresponding results indicate that the symmetry of the output waveform is under 10 ns, the response time is under 100 ns, and the maximum detection distance is 22 m. Further, experimental results demonstrate the superiority of omnidirectional sensors over planar ones in complex environments, successfully receiving 360° laser signals. The positional accuracy of the common point to be measured on the top of the omnidirectional sensor is confirmed to exceed 0.05 mm, and the accuracy of the angle of attitude exceeds 0.04°. Using the laser tracker, the measurement accuracy of the system is verified to be better than 0.3 mm. When rotating in the horizontal and pitch directions, the measurement accuracy is better than 0.35 mm and 0.47 mm, respectively, fulfilling the sub-millimeter precision requirement and expanding the application scope of distributed laser measurement systems.

A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features.

This paper presents a visual compass method utilizing global features, specifically spherical moments. One of the primary challenges faced by photometric methods employing global features is the variation in the image caused by the appearance and disappearance of regions within the camera's field of view as it moves. Additionally, modeling the impact of translational motion on the values of global features poses a significant challenge, as it is dependent on scene depths, particularly for non-planar scenes. To address these issues, this paper combines the utilization of image masks to mitigate abrupt changes in global feature values and the application of neural networks to tackle the modeling challenge posed by translational motion. By employing masks at various locations within the image, multiple estimations of rotation corresponding to the motion of each selected region can be obtained. Our contribution lies in offering a rapid method for implementing numerous masks on the image with real-time inference speed, rendering it suitable for embedded robot applications. Extensive experiments have been conducted on both real-world and synthetic datasets generated using Blender. The results obtained validate the accuracy, robustness, and real-time performance of the proposed method compared to a state-of-the-art method.

Research on Omnidirectional Stereo Measurement Using Convex Mirrors and Vertical Disparity.

We propose an omnidirectional measurement method without blind spots by using a convex mirror, which in principle does not cause chromatic aberration, and by using vertical disparity by installing cameras at the top and bottom of the image. In recent years, there has been significant research in the fields of autonomous cars and robots. In these fields, three-dimensional measurements of the surrounding environment have become indispensable. Depth sensing with cameras is one of the most important sensors for recognizing the surrounding environment. Previous studies have attempted to measure a wide range of areas using fisheye and full spherical panoramic cameras. However, these approaches have limitations such as blind spots and the need for multiple cameras to measure all directions. Therefore, this paper describes a stereo camera system that uses a device capable of taking an omnidirectional image with a single shot, enabling omnidirectional measurement with only two cameras. This achievement was challenging to attain with conventional stereo cameras. The results of experiments confirmed an improvement in accuracy of up to 37.4% compared to previous studies. In addition, the system succeeded in generating depth image that can recognize distances in all directions in a single frame, demonstrating the possibility of omnidirectional measurement with two cameras.

Dynamic Focusing (DF) Cone-Based Omnidirectional Fingertip Pressure Sensor with High Sensitivity in a Wide Pressure Range.

It is essential to detect pressure from a robot's fingertip in every direction to ensure efficient and secure grasping of objects with diverse shapes. Nevertheless, creating a simple-designed sensor that offers cost-effective and omnidirectional pressure sensing poses substantial difficulties. This is because it often requires more intricate mechanical solutions than when designing non-omnidirectional pressure sensors of robot fingertips. This paper introduces an innovative pressure sensor for fingertips. It utilizes a uniquely designed dynamic focusing cone to visually detect pressure with omnidirectional sensitivity. This approach enables cost-effective measurement of pressure from all sides of the fingertip. The experimental findings demonstrate the great potential of the newly introduced sensor. Its implementation is both straightforward and uncomplicated, offering high sensitivity (0.07 mm/N) in all directions and a broad pressure sensing range (up to 40 N) for robot fingertips.

Omnidirectional Continuous Movement Method of Dual-Arm Robot in a Space Station.

The burgeoning complexity of space missions has amplified the research focus on robots that are capable of assisting astronauts in accomplishing tasks within space stations. Nevertheless, these robots grapple with substantial mobility challenges in a weightless environment. This study proposed an omnidirectional continuous movement method for a dual-arm robot, inspired by the movement patterns of astronauts within space stations. On the basis of determining the configuration of the dual-arm robot, the kinematics and dynamics model of the robot during contact and flight phases were established. Thereafter, several constraints are determined, including obstacle constraints, prohibited contact area constraints, and performance constraints. An optimization algorithm based on the artificial bee colony algorithm was proposed to optimize the trunk motion law, contact point positions between the manipulators and the inner wall, as well as the driving torques. Through the real-time control of the two manipulators, the robot is capable of achieving omnidirectional continuous movement across various inner walls with complex structures while maintaining optimal comprehensive performance. Simulation results demonstrate the correctness of this method. The method proposed in this paper provides a theoretical basis for the application of mobile robots within space stations.

Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices.

This paper considers the task of appearance-based localization: visual place recognition from omnidirectional images obtained from catadioptric cameras. The focus is on designing an efficient neural network architecture that accurately and reliably recognizes indoor scenes on distorted images from a catadioptric camera, even in self-similar environments with few discernible features. As the target application is the global localization of a low-cost service mobile robot, the proposed solutions are optimized toward being small-footprint models that provide real-time inference on edge devices, such as Nvidia Jetson. We compare several design choices for the neural network-based architecture of the localization system and then demonstrate that the best results are achieved with embeddings (global descriptors) yielded by exploiting transfer learning and fine tuning on a limited number of catadioptric images. We test our solutions on two small-scale datasets collected using different catadioptric cameras in the same office building. Next, we compare the performance of our system to state-of-the-art visual place recognition systems on the publicly available COLD Freiburg and Saarbrücken datasets that contain images collected under different lighting conditions. Our system compares favourably to the competitors both in terms of the accuracy of place recognition and the inference time, providing a cost- and energy-efficient means of appearance-based localization for an indoor service robot.

Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods.

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.

Research on Transformer Omnidirectional Partial Discharge Ultrasound Sensing Method Combining F-P Cavity and FBG.

To achieve omnidirectional sensitive detection of partial discharge (PD) in transformers and to avoid missing PD signals, a fiber optic omnidirectional sensing method for PD in transformers combined with the fiber Bragg grating (FBG) and Fabry-Perot (F-P) cavity is proposed. The fiber optic omnidirectional sensor for PD as a triangular prism was developed. The hollow structure of the probe was used to insert a single-mode fiber to form an F-P cavity. In addition, the three sides of the probe were used to form a diaphragm-type FBG sensing structure. The ultrasound sensitization diaphragm was designed based on the frequency characteristics of PD in the transformer and the vibration model of the diaphragm in the liquid environment. The fiber optic sensing system for PD was built and the performance test was conducted. The results show that the resonant frequency of the FBG acoustic diaphragm is around 20 kHz and that of the F-P cavity acoustic diaphragm is 94 kHz. The sensitivity of the developed fiber optic sensor is higher than that of the piezoelectric transducer (PZT). The lower limit of PD detection is 68.72 pC for the FBG sensing part and 47.97 pC for the F-P cavity sensing part. The directional testing of the sensor and its testing within a transformer simulation model indicate that the proposed sensor achieves higher detection sensitivity of PD in all directions. The omnidirectional partial discharge ultrasound sensing method proposed in this paper is expected to reduce the missed detection rate of PD.

Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

Design and Implementation of an Integrated Control System for Omnidirectional Mobile Robots in Industrial Logistics.

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.

Multiscale Plasmonic Refractory Nanocomposites for High-Temperature Solar Photothermal Conversion.

Development of a refractory selective solar absorber (RSSA) is the key to unlock high-temperature solar thermal and thermochemical conversion. The fundamental challenge of RSSA is the lack of design and fabrication guidelines to simultaneously achieve omnidirectional, broadband solar absorption and sharp spectral selectivity at the desired cutoff wavelength. Here, we realize a ruthenium-carbon nanotube (Ru-CNT) nanocomposite RSSA with multiscale nanoparticle-on-nanocavity plasmonic modes. Ru conformally coated on the sidewalls of CNTs enables a spoof surface plasmon polariton mode for spectra selectivity; Ru nanoparticles formed at the tips of CNTs enable a localized surface plasmon resonance mode and plasmon hybridization for omnidirectional broadband solar absorption. The fabricated Ru-CNT RSSA has a total solar absorption (TSA) of 96.1% with sharp spectral cutoff at 2.21 µm. The TSA is maintained at over 90% for an incident angle of 56°. Our findings therefore guide full-spectrum optical and thermal control from visible to the far-infrared.

Characteristics of a Rollable Dielectric Barrier Discharge Plasma and Its Effects on Spinach-Seed Germination.

We investigated the characteristics of a rollable dielectric barrier discharge (RDBD) and evaluate its effects on seed germination rate and water uptake. The RDBD source was composed of a polyimide substrate and copper electrode, and it was mounted in a rolled-up structure for omnidirectional and uniform treatment of seeds with flowing synthetic air gas. The rotational and vibrational temperatures were measured to be 342 K and 2860 K, respectively, using optical emission spectroscopy. The chemical species analysis via Fourier-transform infrared spectroscopy and 0D chemical simulation showed that O3 production was dominant and NOx production was restrained at the given temperatures. The water uptake and germination rate of spinach seeds by 5 min treatment of RDBD was increased by 10% and 15%, respectively, and the standard error of germination was reduced by 4% in comparison with the controls. RDBD enables an important step forward in non-thermal atmospheric-pressure plasma agriculture for omnidirectional seed treatment.