GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement

High-resolution multi-modality information acquired by vision-based tactile sensors can support more dexterous manipulations for robot fingers. Optical flow is low-level information directly obtained by vision-based tactile sensors, which can be transformed into other modalities like force, geometry and depth. Current vision-tactile sensors employ optical flow methods from OpenCV to estimate the deformation of markers in gels. However, these methods need to be more precise for accurately measuring the displacement of markers during large elastic deformation of the gel, as this can significantly impact the accuracy of downstream tasks. This study proposes a self-supervised optical flow method based on deep learning to achieve high accuracy in displacement measurement for vision-based tactile sensors. The proposed method employs a coarse-to-fine strategy to handle large deformations by constructing a multi-scale feature pyramid from the input image. To better deal with the elastic deformation caused by the gel, the Helmholtz velocity decomposition constraint combined with the elastic deformation constraint are adopted to address the distortion rate and area change rate, respectively. A local flow fusion module is designed to smooth the optical flow, taking into account the prior knowledge of the blurred effect of gel deformation. We trained the proposed self-supervised network using an open-source dataset and compared it with traditional and deep learning-based optical flow methods. The results show that the proposed method achieved the highest displacement measurement accuracy, thereby demonstrating its potential for enabling more precise measurement of downstream tasks using vision-based tactile sensors

An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator

Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input bias φ and an asymmetric factor ΔΦ into the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO) algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour

Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements

Day respiration (Rd) is an important parameter in leaf ecophysiology. It is difficult to measure directly and is indirectly estimated from gas exchange (GE) measurements of the net photosynthetic rate (A), commonly using the Laisk method or the Kok method. Recently a new method was proposed to estimate Rd indirectly from combined GE and chlorophyll fluorescence (CF) measurements across a range of low irradiances. Here this method is tested for estimating Rd in five C3 and one C4 crop species. Values estimated by this new method agreed with those by the Laisk method for the C3 species. The Laisk method, however, is only valid for C3 species and requires measurements at very low CO2 levels. In contrast, the new method can be applied to both C3 and C4 plants and at any CO2 level. The Rd estimates by the new method were consistently somewhat higher than those by the Kok method, because using CF data corrects for errors due to any non-linearity between A and irradiance of the used data range. Like the Kok and Laisk methods, the new method is based on the assumption that Rd varies little with light intensity, which is still subject to debate. Theoretically, the new method, like the Kok method, works best for non-photorespiratory conditions. As CF information is required, data for the new method are usually collected using a small leaf chamber, whereas the Kok and Laisk methods use only GE data, allowing the use of a larger chamber to reduce the noise-to-signal ratio of GE measurements

NODES CONTROL ALGORITHM DESIGN BASED COVERAGE AND CONNECTIVITY OF WIRELESS SENSOR NETWORK

Coverage and connectivity are two important problems in wireless sensor network. This paper focuses on the wireless sensor network communication radius in the high density of sensor nodes deployed randomly and two times smaller than the sensing radius; put forward a distributed k coverage multi connected node deployment algorithm based on grid. Simulation results show that the algorithm in this paper while guaranteeing the wireless sensor network coverage and connectivity can reduce the number of the active state nodes effectively, prolong the wireless sensor network lifetime. Theoretical analysis results show that this article nodes deployment algorithm achieves multi connecte

Materials, Devices and Systems of Soft Bioelectronics for Precision Therapy

The potential applications of soft bioelectronics in biomedical research and clinical trials have inspired a great deal of research interest in the past decade. While there has been significant amount of work in the fabrication and characterization of soft and stretchable sensors for monitoring of physical conditions and vital signs of human body, the development of soft bioelectronics based medical treatment and intervention systems has just begun. In addition to health monitoring, active treatments are essential for disease control in the healthcare domain, and medical therapy and surgery realized by sophisticated soft bioelectronic systems are better demonstrations of their utility in healthcare. In this Research News, we summarize recent key research achievements in soft bioelectronics enabled precision therapy, with emphasis on drug delivery, therapeutic and surgical mechanisms and tools enabled by integrated systems. Challenges in technology development and prospects for commercialization are also discussed

Materials, Devices and Systems of Soft Bioelectronics for Precision Therapy

The potential applications of soft bioelectronics in biomedical research and clinical trials have inspired a great deal of research interest in the past decade. While there has been significant amount of work in the fabrication and characterization of soft and stretchable sensors for monitoring of physical conditions and vital signs of human body, the development of soft bioelectronics based medical treatment and intervention systems has just begun. In addition to health monitoring, active treatments are essential for disease control in the healthcare domain, and medical therapy and surgery realized by sophisticated soft bioelectronic systems are better demonstrations of their utility in healthcare. In this Research News, we summarize recent key research achievements in soft bioelectronics enabled precision therapy, with emphasis on drug delivery, therapeutic and surgical mechanisms and tools enabled by integrated systems. Challenges in technology development and prospects for commercialization are also discussed