Contact Detection Research Articles

STEP: State Estimator for Legged Robots Using a Preintegrated Foot Velocity Factor

Wepropose a novel state estimator for legged robots, <i>STEP</i>, achieved through a novel preintegrated foot velocity factor. In the preintegrated foot velocity factor, the usual non-slip assumption is not adopted. Instead, the end effector velocity becomes observable by exploiting the body speed obtained from a stereo camera. In other words, the preintegrated end effector&#x2019;s pose can be estimated. Another advantage of our approach is that it eliminates the necessity for a contact detection step, unlike the typical approaches. The proposed method has also been validated in harsh-environment simulations and real-world experiments containing uneven or slippery terrains.

Tactile Super-Resolution Model for Soft Magnetic Skin

Tactile sensors of high spatial resolution can provide rich contact information in terms of accurate contact location and force magnitude for robots. However, achieving a high spatial resolution normally requires a high density of tactile sensing cells (or taxels), which will inevitably lead to crowded wire connections, more data acquisition time and probably crosstalk between taxels. An alternative approach to improve the spatial resolution without introducing a high density of taxels is employing super-resolution technology. Here, we propose a novel tactile super-resolution method based on a sinusoidally magnetized soft magnetic skin, by which we have achieved a 15-fold improvement of localization accuracy (from 6 mm to 0.4 mm) as well as the ability to measure the force magnitude. Different from the existing super-resolution methods that rely on overlapping signals of neighbouring taxels, our model only relies on the local information from a single 3-axis taxel and thereby can detect multipoint contact applied on neighboring taxels and work properly even when some of the neighbouring taxels near the contact position are damaged (or unavailable). With this property, our method would be robust to damage and could potentially benefit robotic applications that require multipoint contact detection.

Ultrasensitive Capacitive Sensor Composed of Nanostructured Electrodes for Human–Machine Interface

AbstractHuman–machine interface requires various sensors for communication, manufacturing and environmental control, and health and safety monitoring. Capacitive sensors have been used to detect touch, distance, geometry, electric property, and environmental parameters. However, highly sensitive proximity detection with a small form factor has always been a challenge. This paper presents a capacitive sensor composed of a nanostructured electrode array for contact and noncontact detection. In the sensor configuration, the nanostructured electrode is made of high aspect ratio cellulose fibers embedded with carbon nanotubes. The complementary electrode is designed to be smaller in surface area for high sensitivity. Based on the analysis, the unique sensing mechanism is shown to enhance the proximity sensitivity for target detection. A pair of asymmetrically designed electrodes are characterized and compared with the traditional symmetric electrodes for proximity and contact detection of human hands. The sensor performance is also characterized for detecting water mass in glass and metal cups. In the end, a smart pad that can recognize human gestures, gait, and water mass with unprecedented sensitivity is demonstrated.

Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning

Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way humans and machines interact can help democratize robotics. With machine and deep learning techniques, robots will more easily adapt to new tasks, conditions, and environments. In this paper, we develop, implement, and evaluate the performance of the machine-learning-based HRI model in a collaborative environment. Specifically, we examine five supervised machine learning models viz. the ensemble of bagging trees (EBT) model, the k-nearest neighbor (kNN) model, the logistic regression kernel (LRK), the fine decision trees (FDT), and the subspace discriminator (SDC). The proposed models have been evaluated on an ample and modern contact detection dataset (CDD 2021). CDD 2021 is gathered from a real-world robot arm, Franka Emika Panda, when it was executing repetitive asymmetric movements. Typical performance assessment factors are applied to assess the model effectiveness in terms of detection accuracy, sensitivity, specificity, speed, and error ratios. Our experiential evaluation shows that the ensemble technique provides higher performance with a lower error ratio compared with other developed supervised models. Therefore, this paper proposes an ensemble-based bagged trees (EBT) detection model for classifying physical human–robot contact into three asymmetric types of contacts, including noncontact, incidental, and intentional. Our experimental results exhibit outstanding contact detection performance metrics scoring 97.1%, 96.9%, and 97.1% for detection accuracy, precision, and sensitivity, respectively. Besides, a low prediction overhead has been observed for the contact detection model, requiring a 102 µS to provide the correct detection state. Hence, the developed scheme can be efficiently adopted through the application requiring physical human–robot contact to give fast accurate detection to the contacts between the human arm and the robot arm.

Improving Accuracy Of Fluid Contact Determination Through The Use Of An Auxiliary Transform Method

Fluid contact(s) in reservoir sets the lower limit above which an accumulation of hydrocarbon (i.e. oil or gas) has the maximum level of mobility under a specific circumstance. This mobile hydrocarbon determines the reservoir’s production feasibility. In this light, an accurate knowledge over position of fluid contact contributes to accuracy in the estimation of initial hydrocarbon in place and its corresponding reserves. Determination of fluid contact in reservoir may utilize any available sources of information but well pressure survey data is usually regarded as the primary source of information. Despite the importance, the data is sometimes not in ideal condition usually marked by absence in clear change of pressure gradient and/or data scatter for various reasons. The use of Hough transform – as introduced by Kang and Xue (2009) – for supporting fluid contact detection can solve the problem. In this study, the method that is usually used for, among others, recognizing regular shapes in images has been successfully applied for fluid contact detection. The study uses three sets of data with different level of difficulty, and the technique proves to work well for the all cases. The study also shows that the Hough transform can be used reliably in a simple way without employing the full weight of it.

Generation of synthetic training data for SEEG electrodes segmentation.

Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used to locate epileptogenic zones. An accurate identification of the metallic contacts recording the SEEG signal is crucial to ensure effectiveness of the upcoming treatment. However, due to the presence of metal, post-operative CT scans contain strong streak artefacts that interfere with deep learning segmentation algorithms and require a lot of training data to distinguish from actual contacts. We propose a method to generate synthetic data and use them to train a neural network to precisely locate SEEG electrode contacts. Random electrodes were generated following manufacturer's specifications and dimensions and placed in acceptable regions inside metal-free CT images. Metal artefacts were simulated in the generated data set using radon transform, beam hardening, and filtered back projection. A UNet neural network was trained for the contacts segmentation task using various training set-ups combining real data, basic augmented data, and synthetic data. The results were compared. We reported a higher accuracy when including synthetic data during the network training, while training only on real and basic augmented data more often led to misclassified artefacts or missed contacts. The network segments post-operative CT slices in less than 2 s using 4 GeForce RTX2080 Ti GPUs and in under a minute using a standard PC with GeForce GTX1060. Using synthetic data to train the network significantly improves contact detection and segmentation accuracy.

Quadrupedal Walking over Complex Terrain with a Quasi-Direct Drive Actuated Robot

In this paper, we present our approach to achieve autonomous walking over complex terrain on the quadrupedal robot, LLAMA. LLAMA is a prototype robot designed by NASA Jet Propulsion Lab as part of the Army Research Laboratory’s Robotics Collaborative Technology Alliance. One of the major objectives of this robot is to be capable of traversing complex terrain autonomously to enable operating alongside a human squadron. This goal requires the robot to be capable of identifying practical footholds according to the environment, which may be sparse, and using these for walking. To accomplish this end, we first introduce two new contact planners. One is based on either desired body path plans; the other an A* graph-search based planner that find contacts over rough terrain given the environmental constraints. We then plan a dynamic trajectory using a custom Divergent Component of Motion planner, which is tracked using a whole-body inverse-dynamics control framework. We also introduce new methods for maintaining balance by adjusting step position and timing. We additionally discuss our approach for contact detection without the use of force sensors. We highlight the results in several experiments, both in our laboratory and at the RCTA Capstone Event at the Camp Lejeune Marine Corps base. We conclude with a discussion of these results, specific implementation problems, and lessons learned when developing such a control architecture on a quadruped.

Numerical analysis of configuration for steel lazy-wave riser in deepwater

ABSTRACT The steel lazy-wave riser (SLWR) is modelled by nonlinear large-deformation beam theory. The equilibrium equations of the SLWR are established based on Minimum Total Potential Energy Principle. The numerical solution adopts an incremental iterative method, which can effectively deal with the riser-seabed interaction and avoid the complexity of contact detection. The method can be used for parameter optimisation. Firstly, introduced the overall configuration and characteristics of SLWR. Secondly, discuss the different working conditions of SLWR. Lastly, it describes how to choose a reasonable range of parameter research and determine each optimal configuration option. The method was verified with Orcaflex. Through static analysis, the effects of water depth, horizontal span, upper catenary length, extreme sea conditions, and motion of vessel on the configuration of the riser, top tension, bending moment, and shear force distribution are obtained.

VContact: Private WiFi-Based IoT Contact Tracing With Virus Lifespan

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close contacts</i> . Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vContact as an Android software development kit and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan.

A new discrete element modelling approach to simulate the behaviour of dense assemblies of true polyhedra

A new molecular dynamics-like modelling approach aimed at simulating the mechanical behaviour of true polyhedra dense assemblies is presented. Thanks to an improved version of the Gilbert-Johnson-Keerthi contact detection algorithm called GJK − TD, this approach involves no edge or corner rounding and accounts for multipoint face − face or edge − face contacts between any set of two particles. Furthermore, torques equations are simply and efficiently solved using an improved leap-frog Verlet non-iterative method suggested by Omelyan [I.P. Omelyan, Molecular Simulation, 22 : 3, 213–236 (1999)], in which no periodic renormalization of the quaternions is necessary. The potential of this new discrete element approach is then highlighted by simulating the gravity packing of frictionless polyhedra and the gavity flow of frictional polyhedra down an incline, and comparing the results with those reported in the literature. Of particular interest in the stationary flow regime, a linear decrease of the bulk solid fraction and a power law decrease of the bulk coordination number with increasing inertial number are observed, thus generalizing to polyhedra these observations initially made with disks in plane shear flow by da Cruz and co-workers [F.da Cruz, S. Emam, M. Prochnow, J.N. Roux and F. Chevoir, Phys. Rev. E 72, 021309, (2005)]. Beyond this regime, in the collisional regime, the granular temperature was found to achieve its maximum a few particle diameter above the rough bottom, suggesting the localization of significant particle agitation close to the flowing bed.

HiCBin: binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. We develop HiCBin, a novel open-source pipeline, to resolve high-quality MAGs utilizing Hi-C contact maps. HiCBin employs the HiCzin normalization method and the Leiden clustering algorithm and includes the spurious contact detection into binning pipelines for the first time. HiCBin is validated on one synthetic and two real metagenomic samples and is shown to outperform the existing Hi-C-based binning methods. HiCBin is available at https://github.com/dyxstat/HiCBin.

A procedure for efficient generation and behavioral evaluation of ultra‐packed ellipsoidal particle systems

AbstractMany engineering applications demand ultra‐packing of particles into a matrix. In general, efficiently simulating the random close packing of non‐spherical particles, and with 60% or greater volume loading remains a challenge. In this article, we describe an efficient procedure that achieves ultra‐packing of ellipsoidal particle systems of the order of – particles on a desktop computer. A significant challenge to ellipsoidal particle packing problem is particle‐particle contact detection. We describe an algebraic intersection detection method for ellipsoidal particles and couple it with a heuristic constructive algorithm to generate densely packed microstructures with ellipsoidal fillers. The effective behavior of the generated microstructures is then numerically estimated using an efficient random‐network model that is valid for high‐contrast particulate composites in near percolation arrangement. Finally, the algorithms are applied to evaluate the effective thermal conductivity of ellipsoidal particle filled composites used as thermal interface materials. Simulations of lognormally distributed particle systems with varying aspect ratios of ellipsoidal fillers are systematically carried out to draw conclusions on impact of particle shapes.

Online Learning for Foot Contact Detection of Legged Robot Based on Data Stream Clustering.

Foot contact detection is critical for legged robot running control using state machine, in which the controller uses different control modules in the leg flight phase and landing phase. This paper presents an online learning framework to improve the rapidity of foot contact detection in legged robot running. In this framework, the Gaussian mixture model with three sub-components is adopted to learn the contact data vectors corresponding to running on flat ground, running upstairs, and running downstairs. An online data stream learning algorithm is used to update the model. To deal with the difficulty in obtaining contact data at landing moment online, a “trace back” module is designed to trace back the contact data in the memory stack until the data meet with the probability contact criterion. To test if the foot is in contact with the ground, a projection method is proposed. The acquiring data vector during the leg flight phase is projected onto an independent random vector space, and the contact event is triggered if all projected random variables fall within 1.5σ of the corresponding Gaussian distribution. Experiments on a legged robot show that the presented algorithm can predict the foot contact 16 ms in advance compared with the prediction using only leg force, which will ease the controller design and enhance the stability of legged robot control.

Superquadric DEM-SPH coupling method for interaction between non-spherical granular materials and fluids

The research on the coupling method of non-spherical granular materials and fluids aims to predict the particle–fluid interaction in this study. A coupling method based on superquadric elements is developed to describe the interaction between non-spherical solid particles and fluids. The discrete element method (DEM) and the smoothed particle hydrodynamics (SPH) are adopted to simulate granular materials and fluids. The repulsive force model is adopted to calculate the coupling force and then a contact detection method is established for the interaction between the superquadric element and the fluid particle. The contact detection method captures the shape of superquadric element and calculates the distance from the fluid particle to the surface of superquadric element. Simulation cases focusing on the coupling force model, energy transfer, and large-scale calculations have been implemented to verify the validity of the proposed coupling method. The coupling force model accurately represents the water entry process of a spherical solid particle, and reasonably reflects the difference of solid particles with different shapes. In the water entry process of multiple solid particles, the total energy of the water entry process of multiple solid particles tends to be stable. The collapse process of the partially submerged granular column is simulated and analyzed under different parameters. Therefore, this coupling method is suitable to simulate fluid–particle systems containing solid particles with multiple shapes.

Invasion genetics of the Asian hornet Vespa velutina nigrithorax in Southern Europe

In 2004, Vespa velutina was first seen in France. Since then, this fierce honey bee predator spread across many countries, giving rise to one of the most phenomenal insect invasions in Europe. An early study in France showed a genetically depauperate population, originating from a single multi-mated queen introduced from China. Here, we further unveil V. velutina invasion genetics in Europe by surveying the Iberian and Italian peninsulas using cytonuclear markers. Our results show that the French population acted as the colonists’ source in Spain, Portugal and Italy, leading to rejecting the hypothesis of multiple introductions from the native range. While Spain and Italy were colonized predominantly by leading-edge expansions from the French core population, in Portugal the invasion started from long-distance jump. Both processes were accompanied by a significant reduction in genetic diversity, with stronger losses for Portugal (Ar = 17.4%; uHe = 42.3%) than for Spain (Ar = 9.0%; uHe = 20.6%) or Italy (Ar = 16.3%; uHe = 26.8%). Signatures of differentiation and population structure, associated to the founding event in Portugal, enabled detection of secondary contact between the front derived from the primary propagule introduced in France and the front derived from the secondary propagule introduced in Portugal. Detection of first-generation migrants in the three countries suggests continuous gene flow that is bringing in new alleles, and this effect is stronger in Portugal, as reflected by a 20.3% increase in allelic richness. Overall, this study provides further insights into the invasion genetics of V. velutina in Europe, which can aid developing strategies to manage this major threat to beekeeping.

A modified discrete element method for concave granular materials based on energy-conserving contact model

The development of a general discrete element method for irregularly shaped particles is the core issue of the simulation of the dynamic behavior of granular materials. The general energy-conserving contact theory is used to establish a universal discrete element method suitable for particle contact of arbitrary shape. In this study, three dimentional (3D) modeling and scanning techniques are used to obtain a triangular mesh representation of the true particles containing typical concave particles. The contact volume-based energy-conserving model is used to realize the contact detection between irregularly shaped particles, and the contact force model is refined and modified to describe the contact under real conditions. The inelastic collision processes between the particles and boundaries are simulated to verify the robustness of the modified contact force model and its applicability to the multi-point contact mode. In addition, the packing process and the flow process of a large number of irregular particles are simulated with the modified discrete element method (DEM) to illustrate the applicability of the method of complex problems.

Diagnostic performance of two serological assays for the detection of SARS-CoV-2 specific antibodies: surveillance after vaccination

Massive vaccination programs are being carried out to limit the SARS-CoV-2 pandemic that started in December 2019. Serological tests are of major importance as an indicator of circulation of the virus and to assess how vaccine-induced immunity progresses.An Enzyme-Linked Immunosorbent Assay (ELISA) and a Lateral Flow Assay (LFA) have been developed based on the SARS-CoV-2 recombinant Receptor Binding Domain (RBD) and the combination of Spike and Nucleoprotein, respectively. The validation with 1272 serum samples by comparison with INgezim COVID 19 DR showed good diagnostic performance (sensitivity: 93.2%-97.2%; specificity: 98.3%-99.3%) for detection of previous contact with SARS-CoV-2. Moreover, according to our results, these assays can help in the serosurveillance during and after vaccination, by detecting the humoral immune response as soon as 15 days postvaccination and identifying low-respondents. Hence, these tests could play a key role in the progression to a COVID-19 free world, helping to adjust future vaccination protocols.

Feasibility study on multi-touch ultrasound large-panel touchscreen using guided lamb waves

In this paper, we propose a transmission method, analysis, and implementation of an ultrasound touchscreen system based on Lamb waves. The basic principle of this system is to use the signals reflected from the fingertip contact. The inverted phase transmission method, which alternately transmits two opposite phases of input signals, was proposed to improve the signal contrast of the reflected signal from the fingertip. Experimental validation of this system was demonstrated on a 40-inch glass plate (800 mm × 600 mm) containing eight pairs of sensors. For imaging and localization, a simple and common method is used: the delay and sum method for imaging and the center of mass for localization. Compared with existing ultrasound technology, the proposed system is implemented in a large panel without using the training or prepared database. The system also provides reliable contact detection, simplicity, and other advantages of the present acoustic techniques.

Predicting pathologic venous invasion before pancreatectomy with venous resection: When does radiology tell the truth?

BackgroundPatients factors in addition to radiological characteristics could predict the presence of pathologic venous invasion in patients undergoing pancreatectomy with venous resection. MethodsWe tested the predictive value of 6 radiological classification methods for predicting pathologic venous invasion—the Nakao, Ishikawa, MD Anderson, Lu, Raptopoulos, and National Comprehensive Cancer Network methods—on a cohort of 198 pancreatectomies (160 pancreaticoduodenectomies and 38 total pancreatectomies) with venous resection for pancreatic adenocarcinomas. Radiological and clinical factors determining pathologic venous invasion were identified by multivariable logistic analysis. ResultsPathologic venous invasion was detected in 124 patients (63.2%). The multivariable logistic regression analysis identified Lu classification (odds ratio = 1.77, 95% confidence interval =1.34–2.35; P < .0001), elevated serum CA19-9 values (odds ratio = 1.97, 95% confidence interval = 1.00–3.90; P = .04), and preoperative neoadjuvant chemotherapy (odds ratio = 0.38, 95% confidence interval = 0.18–0.79; P = .009) as independent factors associated with pathologic venous invasion. Radiological tumor-vessel contact greater than 50% of the circumference or venous wall deformity was associated with a significantly higher rate of pathological venous invasion (80% vs 52%; P < .0001), deeper (media-intima) venous invasion (47% vs 25%; P < .0001), R1 resection (58% vs 41%; P = .03), higher transfusions (84% vs 66%; P = .005), and arterial resection rates (43% vs 27%; P < .0001). Tumor-vein circumference contact of >50% and/or venous wall deformity was still associated with significantly higher rates of pathologic venous invasion, regardless of whether neoadjuvant chemotherapy was used or not and CA19-9 normalized or not under preoperative treatment. ConclusionPreoperative radiological detection of tumor-vein circumference contact >50% and/or venous wall deformity is associated with up to 80% of cases of pathological venous invasion. The combination of radiologic features with biological (CA19-9) and clinical (presence of preoperative chemotherapy) factors could better refine preoperatively the need for venous resection.

Quantum speed limit of the double quantum dot in pure dephasing environment under measurement

The quantum speed limit (QSL) of the double quantum dot (DQD) system has been theoretically investigated by adopting the detection of the quantum point contact (QPC) in the pure dephasing environment. The Mandelstam–Tamm (MT) type of the QSL bound which is based on the trace distance has been extended to the DQD system for calculating the shortest evolving time. The increase of decoherence rate can weaken the capacity for potential speedup (CPS) and delay the evolving process due to the frequently measurement localizing the electron in the DQD system. The system needs longer time to evolve to the target state as the enhancement of dephasing rate, because the strong interaction between pure dephasing environment and the DQD system could vary the oscillation of the electron. Increasing the dephasing rate can sharp the QSL bound, but the decoherence rate would weaken the former effect and vice versa. Moreover, the CPS would be raised by increasing the energy displacement, while the enhancement of the coupling strength between two quantum dots can diminish it. It is interesting that there has an inflection point, when the coupling strength is less than the value of the point, the increasing effect of the CPS from the energy displacement is dominant, otherwise the decreasing tendency of the CPS is determined by the coupling strength and suppress the action of the energy displacement if the coupling strength is greater than the point. Our results provide theoretical reference for studying the QSL time in a semiconductor device affected by numerous factors.