Extrinsic Sensor Research Articles

Piezoresistivity and piezopermittivity of cement-based sensors under quasi-static stress and changing moisture

Integrated cement-based sensors offer an economic alternative to extrinsic sensors for health monitoring applications in concrete structures due to their high strength to cost ratio, geometrical versatility, low shrinkage, and natural compatibility. Nonetheless, their performance under in-service conditions were in lack of investigations. While the piezoresistivity (change in resistance with stress) has been commonly used for mechanical sensing, the piezopermittivity (change in capacitive reactance with stress) is rarely characterized. Exploiting the high relative permittivity and electrical conductivity of carbon fibre reinforced cement-based sensors, this study investigates the piezoresistivity and piezopermittivity under changing stress and moisture using electrochemical impedance spectroscopy (EIS). Two types of sensors were evaluated: one containing 0.5 vol% of carbon fibres whose electrical conductivity was ionically dominant, and another with electronically dominant (1.2 vol% of carbon fibres) conductivity. Results highlighted that the piezopermittivity is “moisture content-dominant” whilst the piezoresistivity is “fibre content-dominant”. As the moisture content decreased, the sensitivity of piezopermittivity for both sensor types decreased, while the sensitivity of piezoresistivity decreased for the ionically dominant sensor but increased for the electronically dominant sensor. The piezoresistivity of the electronically dominant sensor was less sensitive than piezopermittivity at a water saturation of 80%. Conversely, the piezoresistivity of the ionically dominant sensor was more sensitive than piezopermittivity at the tested water saturations ≤ 80%. For the first time, this study presents the combined effects of moisture and fibre content on the pressure sensitive response of cement-based sensors through a dual-phase (i.e., piezoresistivity and piezopermittivity) EIS interpretation technique, providing valuable information to benefit further behaviour prediction and single-effect recognition in the field scenario where the sensors are subject to simultaneous environmental effects causing moisture variations such as temperature and humidity variations, freeze-thawing, and so on.

Learning Observation Model for Factor Graph Based-State Estimation Using Intrinsic Sensors

The navigation system of autonomous mobile robots has appeared challenging when using exteroceptive sensors such as cameras, LiDARs, and radars in textureless and structureless environments. This paper presents a robust state estimation system for holonomic mobile robots using intrinsic sensors based on adaptive factor graph optimization in the degradation scenarios. In particular, the neural networks are employed to learn the observation and noise model using only IMU sensor and wheel encoder data. Investigating the learning model for the holonomic mobile robot is discussed with various neural network architectures. We also explore the neural networks that are far more powerful and have cheaper computing power when using the inertial-wheel encoder sensors. Furthermore, we employ an industrial holonomic robot platform equipped with multiple LiDARs, cameras, IMU, and wheel encoders to conduct the experiments and create the ground truth without a bulky motion capture system. The collected datasets are then implemented to train the neural networks. Finally, the experimental evaluation presents that our solution provides better accuracy and real-time performance than other solutions. <i>Note to Practitioners</i>&#x2014;Autonomous mobile robots need to serve in challenging environments robustly that deny extrinsic sensors such as cameras, LiDARs, and radars. In order to operate in this situation, the navigation system shall rely on the intrinsic sensor as inertial sensor and wheel encoders. Existing conventional methods have combined the intrinsic sensors in the form of the recursive Bayesian filtering technique without adapting these models. Besides, deep learning-based solutions have adopted extensive networks like LSTM or CNN to handle the estimation problem. This work aims to develop a state estimation subsystem of the navigation system for the holonomic mobile robots that utilize the intrinsic sensors in adaptive factor graph optimization. In particular, we present how to join the factor graph efficiently with the learning observation model of IMU and wheel encoder factor. Moreover, the neural networks are introduced to learn the observation model with an IMU and wheel encoder data inputs. We recognize that lightweight neural networks can achieve more power than deep learning techniques using the IMU sensor and wheel encoders. Finally, the neural networks are embedded in a factor graph to handle the smoothing state estimation. The proposed system could operate with high accuracy in real time.

Learning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion

Although a wide variety of high-performance state estimation techniques have been introduced recently, the robustness and extension to actual conditions of the estimation systems have been challenging. This paper presents a robust adaptive state estimation framework based on the Type-2 fuzzy inference system and factor graph optimization for autonomous mobile robots. We use the hybrid solution to connect the advantages of the tightly and loosely coupled technique by providing an inertial sensor and other extrinsic sensors such as LiDARs and cameras. In order to tackle the uncertainty input covariance and sensor failures problems, a learnable observation model is introduced by joining the Type-2 FIS and factor graph optimization. In particular, the use of Type-2 Takagi-Sugeno FIS can learn the uncertainty by using particle swarm optimization before adding the observation model to the factor graph. The proposed design consists of four parts: sensor odometry, up-sampling, FIS based-learning observation model, and factor graph-based smoothing. We evaluate our system by using a mobile robot platform equipped with a sensor setup of multiple stereo cameras, an IMU, and a LiDAR sensor. We imitate the LiDAR odometry in structure environments without needing other bulky motion capture systems to learn the observation model of the visual-inertial estimators. The experimental results are deployed in real-world environments to present the accuracy and robustness of the algorithm.

Sensorless force and displacement estimation in soft actuators.

Sensing forms an integral part of soft matter based robots due to their compliance, dependence on loading conditions, and virtually infinite degrees of freedom. Previous studies have developed several extrinsic sensors and embedded them into soft actuators for displacement and force estimation. What has not been investigated is whether soft robots themselves possess intrinsic sensing capabilities, especially in the case of pneumatically powered soft robots. Such an approach, that exploits the inherent properties of a system toward sensing is called sensorless estimation. Here, we introduce sensorless estimation for the first time in pneumatically powered soft actuators. Specifically, we show that the intrinsic properties of pressure and volume can be used to estimate the output force and displacement of soft actuators. On testing this approach with a bending actuator, we observed errors under 10% and 15% for force and displacement estimation respectively, with randomized and previously unseen test conditions. We also show that combining this approach with a conventional embedded sensor improves estimation accuracy due to sensing redundancy. By modelling soft actuators additionally as sensors, this work presents a new, readily implementable sensing modality that helps us better understand the highly complex behaviour of soft matter based robots.

3D Microdisplacement Monitoring of Large Aircraft Assembly with Automated In Situ Calibration

Three-dimensional (3D) microdisplacement monitoring plays a crucial role in the assembly of large aircraft. This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method, which can be applied under various extreme working conditions encountered in the aircraft assembly process, such as compact and obstructed spaces. A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors, which concerns the extrinsic sensor parameters, such as the probe base point (PBP) and the unit displacement vector (UDV). Then, a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization. Finally, calibration and monitoring experiments performed for a tailplane assembly process are reported. The calibration precision for the PBP is better than ±10 μm in the X and Y directions and ±2 μm in the Z direction, and the calibration precision for the UDV is better than 0.07°. Moreover, the accuracy of the 3D microdisplacement monitoring system can reach ±15 μm. In general, this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise, efficient, and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.

3D map building based on extrinsic sensor calibration method and object contour detector with a fully convolutional neural network

In this paper, we propose a simple method to obtain an object’s 3D coordinate information in one image, using one monocular camera and two 2D LiDAR sensors, which are widely used low-cost sensors. An extrinsic calibration method is used for each LiDAR sensor to transfer LiDAR coordinate to camera pixel coordinate in one image. The proportion factor [Formula: see text] is calculated, based on the relationship of pixel from calibration points and distance from detected points of two LiDAR sensors. In order to create a correct 3D map, a deep learning algorithm for real-time object contour detection with a fully Convolutional Neural Network is proposed, through gathering data, labeling data, training model, and testing object contour detector. For 3D map building, by the proportion factor [Formula: see text] and the detector contour feature pixels, the height of the object is obtained. Combining with LiDAR sensor’s detection information, the 3D posture of the object is obtained. Based on the proposed extrinsic sensor calibration method and the object contour detect result, virtual points of the object are calculated. Finally, the experiment results show the 3D coordinate information of the detected single or multiple objects. Assembling sensors and a controller on a Kobuki robot, the indoor 3D map is built based on the proposed methods.

ColoRadar: The direct 3D millimeter wave radar dataset

This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors.

A review on sensory perception for dexterous robotic manipulation

Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and discussed.

Maximum Sum of Evidence-An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data.

The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.

Online Extrinsic Calibration Based on Per-Sensor Ego-Motion Using Dual Quaternions

In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a method to verify the globality of the local approach. Additionally, means for integrating previous knowledge, for example, a common ground plane for planar sensor motion, are described. Our algorithms are evaluated on simulated data and on a publicly available dataset containing RGB-D camera images. Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle. Our evaluation confirms the short run time, state-of-the-art accuracy, as well as online capability of our approach while retaining the global optimality of the solution at any time.

Enhanced sensitivity of magneto-optical sensor using defect induced perovskite metal oxide nanomaterial

Perovskite metal oxide (LiNbO3) nanomaterial is explored as a magneto-optic sensor. The electromagnetic interaction is described by magneto-optic modulation associated with vacancy-induced room temperature ferromagnetism. The guiding and propagation through single mode fiber (SMF) is via the excitation of core modes through the material-mediated cavity and further modulated by external magnetic field. The sensitivity and resolution of the sensor are found to be 186 p.m./mT and 0.51 Oe, respectively. The structure with material integrated optical design resulting in a Fabry-Perrot interferometer (FPI), is implemented as an extrinsic sensor, and sensing mechanism is elaborated.

Design of an All-POF-Fiber Smartphone Multichannel Breathing Sensor With Camera-Division Multiplexing

We report the design and prototyping of an all-fiber-smartphone multiplexed sensor, with the purpose of breathing rate analysis. The architecture is based on a multichannel extrinsic sensor based on intensity modulation. The sensing system is entirely based on plastic optical fiber (POF). The optical hardware is entirely based on the smartphone, using the flashlight LED as the source and the camera as the pixel-based detector. A 3-D printed connector has been fabricated to facilitate light coupling to and from the POF links. A multiplexing concept labeled “camera-division multiplexing” has been implemented and is reported for a three-fiber system: The camera is spatially tessellated, dividing the contribution of each fiber with crosstalk 8.2–15.3 dB. The application for breathing rate detection, with camera ISO set to 800, is proposed and implemented, visualizing the power spectral density of the respiration pattern.

Extrinsic 6DoF calibration of a radar–LiDAR–camera system enhanced by radar cross section estimates evaluation

Autonomous navigation of mobile robots is often based on information from a variety of heterogeneous sensors; hence, extrinsic sensor calibration is a fundamental step in the fusion of such information. In this paper, we address the problem of extrinsic calibration of a radar–LiDAR–camera sensor system. This problem is primarily challenging due to sparse informativeness of radar measurements. Namely, radars cannot extract rich structural information about the environment, while their lack of elevation resolution, that is nevertheless accompanied by substantial elevation field of view, introduces uncertainty in the origin of the measurements. We propose a novel calibration method which involves a special target design and two-step optimization procedure to solve the aforementioned challenges. First step of the optimization is minimization of a reprojection error based on an introduced point–circle geometric constraint. Since the first step is not able to provide reliable estimates of all the six extrinsic parameters, we introduce a second step to refine the subset of parameters with high uncertainty. We exploit a pattern discovered in the radar cross section estimation that is correlated to the missing elevation angle. Additionally, we carry out identifiability analysis based on the Fisher Information Matrix to show minimal requirements on the dataset and to verify the method through simulations. We test the calibration method on a variety of sensor configurations and address the problem of radar vertical misalignment. In the end, we show via extensive experiment analysis that the proposed method is able to reliably estimate all the six parameters of the extrinsic calibration.

A method for UAV multi-sensor fusion 3D-localization under degraded or denied GPS situation

Unmanned aerial vehicles (UAVs) have been developed to be used in complex environments. Continuity of a UAV operation when GPS is degraded or denied is crucial in many applications, such as flying near high buildings and trees, or flying outdoor-to-indoor. In this paper, an algorithm for 3D-localization during transition between indoor and outdoor environments for a UAV is presented. Localization inputs are based on information from GPS, inertial measurement unit, monocular camera, and optical flow sensor. Information is carefully selected using GPS quality indicator method corresponding to the operating environment. After that, a smoothing offset approach is employed to smooth the position estimation. The selected sensors’ data are filtered by indirect extended Kalman filter for localization and extrinsic sensor calibration in real time. Results show a seamless offset convergence of UAV localization for indoor–outdoor transition. Moreover, the proposed method of decision-making to cut off GPS measurement even when it experiences poor signal quality can still outperform conventional GPS-based cutoff method in terms of response time.

Corrugated Fiber Grating for Detection of Lead Ions in Water

Optical fiber fluorescence sensors are widely used for chemical sensing. Intrinsic fluorescence sensors such as evanescent-wave sensors can be used for surface sensing while extrinsic sensors are used for bulk sensing. In intrinsic/extrinsic fluorescence sensors, typically the side-wall/end-face of a single/multimode fiber is used. In this paper, we introduce a novel intrinsic optical fiber fluorescence sensor which uses the side-wall of a large-core multimode fiber to collect the fluorescence from the bulk of samples. The method is to use a corrugated fiber grating, fabricated by a CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> laser, to couple radiating modes to the core modes. Using the sidewall of a fiber helps to fabricate a multitarget or a distributed sensor. We also report the sensing of Lead ions with concentration as low as few ppb in water using a fluorescence turn-on polymer as a chemical probe.

A Mechatronic Platform for Calibration and Performance Test of Extrinsic Tactile Sensor on Prosthesis Hand

This paper presents a mechatronic platform consists of a stimulator and a prosthesis hand holder, which aims at extrinsic flexible tactile sensor calibration and performance test for prosthesis hand application. The former is driven by a linear motor and the latter is driven by the combination of one 3 dimensions of freedom (DoF) motion stage and two rotation stages. A procedure is developed to calibrate flexible tactile sensors attached on the anthropomorphic prosthetic hand, whose surface is curved. Although the flexible tactile sensors can be easily integrated on to the mentioned curved surface due to its flexibility and compliance, they are usually flat fabricated and characterized under the working of flat situation. Curved surface sensor characterization is not directly available from the manufacturer. Furthermore, when tactile sensors are applied, there would be an elastic layer covered on top for protection and improvement of grasping ability. This elastic material essentially deteriorates the characteristics of the sensor under it, which is another important reason for establishing a platform to re-calibrate tactile sensor mounted on prosthetic hand. In this paper, two kinds of commercial tactile sensors are involved which are force sensing resistor (FSR) representing static measurement sensor and polyvinylidene fluoride (PVDF) sensor representing dynamic measurement sensor where a rigid rod with polydimethylsiloxane (PDMS) covered imitates prosthetic finger. The methodology of calibration compares the measured data obtained from tactile sensor with the one from a load cell fixed on the stimulator. The procedure of receptive-field measurement for tactile sensor on the prosthetic hand is shown and the equation for estimating the position and amplitude of surface load on top of elastic cover is established. Different combinations of tactile sensors are implemented on the rigid substance and two motion patterns of stimulator are performed on them, which are indentation and sliding. The performance of developed platform is also analyzed and it shows the ability of testing flexible tactile sensor for prosthesis hand.

Characterization of scintillating X-ray optical fiber sensors.

The paper presents a set of tests carried out in order to evaluate the design characteristics and the operating performance of a set of six X-ray extrinsic optical fiber sensors. The extrinsic sensor we developed is intended to be used as a low energy X-ray detector for monitoring radiation levels in radiotherapy, industrial applications and for personnel dosimetry. The reproducibility of the manufacturing process and the characteristics of the sensors were assessed. The sensors dynamic range, linearity, sensitivity, and reproducibility are evaluated through radioluminescence measurements, X-ray fluorescence and X-ray imaging investigations. Their response to the operating conditions of the excitation source was estimated. The effect of the sensors design and implementation, on the collecting efficiency of the radioluminescence signal was measured. The study indicated that the sensors are efficient only in the first 5 mm of the tip, and that a reflective coating can improve their response. Additional tests were done to investigate the concentricity of the sensors tip against the core of the optical fiber guiding the optical signal. The influence of the active material concentration on the sensor response to X-ray was studied. The tests were carried out by measuring the radioluminescence signal with an optical fiber spectrometer and with a Multi-Pixel Photon Counter.

Spectral eigendecomposition‐based algorithm for cavity estimation in fibre‐optic Fabry‐Pérot pressure sensors

A novel signal processing-based approach for the real-time estimation of air-gap length in fibre optic extrinsic Fabry-Perot pressure sensors is presented; the algorithm is based on eigendecomposition of the spectrum mutual correlation matrix. A pressure accuracy of 6.1 Pa (0.045 mmHg) is achieved in a pressure probe with 1.13 nm/kPa sensitivity, highlighting a 6.4 times improvement over standard quadrature point amplitude tracking by just acting on signal processing.

Coupled Heterogeneous Nanowire–Nanoplate Planar Transistor Sensors for Giant (&gt;10 V/pH) Nernst Response

We offer a comprehensive theory of pH response of a coupled ISFET sensor to show that the maximum achievable response is given by ΔV/ΔpH = 59 mV/pH × α, where 59 mV/pH is the intrinsic Nernst response and α an amplification factor that depends on the geometrical and electrical properties of the sensor and transducer nodes. While the intrinsic Nernst response of an electrolyte/site-binding interface is fundamental and immutable, we show that by using channels of different materials, areas, and bias conditions, the extrinsic sensor response can be increased dramatically beyond the Nernst limit. We validate the theory by measuring the pH response of a Si nanowire-nanoplate transistor pair that achieves >10 V/pH response and show the potential of the scheme to achieve (asymptotically) the theoretical lower limit of signal-to-noise ratio for a given configuration. We suggest the possibility of an even larger pH response based on recent trends in heterogeneous integration on the Si platform.

Optical sensing of hazardous exhaust emissions using a UV based extrinsic sensor

Abstract A fibre-optic sensor for the monitoring of hazardous exhaust gases is described. The sensor based on ultraviolet (UV) differential optical absorption spectroscopy (DOAS) was developed for the simultaneous monitoring of several exhaust gases from passenger cars. Experimental results describing the operation of this sensor with calibrated cylinder gases as well as in a full-size engine are presented. The sensor has been developed to operate within an exhaust environment and have low susceptibility to interferences from other gases present. The lower limit of detection for the sensor was found to be 5 ppm for nitric oxide (NO), 1 ppm for both nitrogen dioxide (NO2) and sulphur dioxide (SO2) and response time was found to be 3.4 s.