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Polypyrrole (PPy) has high electrical conductivity, good environmental stability, and reversible electrochemical redox characteristics, which makes PPy responsive to changes in environmental humidity values. In this work, a humidity sensor with a good response to humidity was prepared using in situ polymerization of PPy in filter paper doping with acetic acid. A possible sensing mechanism of conductive polymers is proposed and the conjecture is verified by experiments. The results demonstrated that the PPy/filter paper-based humidity sensor provides an electron migration channel with the capability of detecting the relative humidity. External humidity stimulation can regulate the electrochemical reaction of PPy. The reduction reaction occurs near the water side to generate the nucleophilic product OH–. The PPy system loses electrons to form carrier directional channels, resulting in changes in the output voltage measured between the two electrodes. The PPy/filter paper-based humidity sensor exhibits the moisture dependent voltage response over a wide range from a relative humidity of 11–98%, and a response/recovery time of 43/51 s as it was placed between relative humidity of 33% and 98%. In addition, unlike the traditional sensing mechanism, the voltage sensing mechanism raised here shows self-powered ability with no need for an additional power unit. This provides a new idea for self-powered sensor devices, and this sensor shows good performance in non-contact sensing applications such as breath detection. [ABSTRACT FROM AUTHOR]

In recent years, the need for portable, low-cost, and eco-friendly devices for testing and monitoring has arisen. Paper-based devices have emerged as a response to these needs due to the properties induced by capillarity, flexibility, disposability, and biodegradability. In this work, the authors explored the possibility of tuning the hygro-mechanical response of paper-based cantilever beams using glycerol. A lumped-parameter model with non-linear stiffness is used to describe the dynamic response of the beams using three parameters. An experimental method based on resonance frequency tests is used to study the influence of glycerol on the dynamic response of four different beam configurations. The obtained results demonstrate that the resonance frequency of paper-based mechanical systems can be easily tuned by the imbibition of a glycerol–water solution. This study could lead to the development of tunable paper-based mechanical systems for specific applications such as energy harvesters and hygro-mechanical-based sensors. [ABSTRACT FROM AUTHOR]

In the active magnetic bearing (AMB)-rotor system, a displacement sensor is one of the indispensable components. It detects the displacement of the rotor in real time and feeds back to the control system to realize the stable suspension of the rotor. However, due to electromagnetic interference, unstable power supply, poor grounding, and uneven rotor surface, the signal runout of the displacement sensor can often occur, resulting in rotor vibration. Given the above problems, this paper establishes the control model of the AMB-rotor system, lists three typical sensor runout signals, then analyzes the influence of different types of sensor runout on the system, and discusses the control methods under different sensor runout conditions. The research results have a specific guiding significance for vibration control of the maglev rotor under sensor runout influence. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT. [ABSTRACT FROM AUTHOR]

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm. [ABSTRACT FROM AUTHOR]

The possibility of EAPap as a vibration sensor based on piezoelectricity was investigated in this article. The EAPap was fabricated by regenerating and tape casting cellulose. The sample was coated by a thin laminating film for packaging. The capacitance of EAPap was measured and compared with commercial PVDF. Relative permittivity of EAPap was 12, which was the same as commercially available PVDF. This reveals that EAPap has sensing potential similar to synthetic piezo polymer film. The simple aluminum cantilevered beam was used for the vibration testing and EAPap was attached on the beam. The original EAPap sensor without grounding and shielding is greatly affected by surrounding noise, especially power noise. The power noise dropped dramatically when the sensor was grounded and shielded. The impulsive response of EAPap provided the correct dynamic characteristics of the beam, especially the twisting mode of the beam was observed clearly even though the EAPap was attached at the center of the beam. This is because the sensing capability of EAPap is based on piezoelectricity which is bidirectional strain characteristics. EAPap sensor based on piezoelectricity provided a great potential as a vibration sensor. [ABSTRACT FROM AUTHOR]

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object's observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE (3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges. [ABSTRACT FROM AUTHOR]

Deep space exploration activities can effectively promote the development and application of space technology, which holds significant scientific and strategic value. Detectors operating in deep space are of long mission periods and substantial distance from Earth, posing challenges in timely resource replenishment. Moreover, scheduled missions may face external uncertainties, hindering their smooth execution. Therefore, resource constraint processing strategies that possess dynamic adjustment capabilities play a crucial role in ensuring the successful execution of deep space detectors. Existing methods mainly focus on studying fixed mission sequences, and the efficiency of planning methods is not high. Specifically, the single uncertainty is only considered, and there is a lack of detection and processing mechanisms, rendering the requirements of deep space detectors inadequate. In this paper, we propose a reverse chain search algorithm, aiming to efficiently plan missions subject to resource constraints and let the mission sequence to possess a certain degree of dynamic repair capability. The results have demonstrated that the resource allocated to each mission is within the resource value range. Compared with the classic algorithm, the proposed algorithm has fewer iterations and whose maximum flow value is accurate. Meanwhile, the potential resource constraints problem during execution can be alerted with the detection and warning system. The missions that were warned are repaired by a dynamic resource repair system, and the proposed algorithm is of higher efficiency and a certain ability to resist external uncertainties. Overall, this paper lays a solid foundation for the processing of resource constraints for deep space detectors. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for satellite sensors' fault detection and diagnosis (FDD) in a fault-tolerant attitude determination system using a nonlinear interacting-multiple-model approach (IMM). Moreover, the fault-tolerant attitude determination system utilizes the IMM approach in the presence of sensors' total fault to estimate faulty sensors and attitude states. This methodology relies on the most conventional satellite attitude sensors including magnetometer, sun sensor, and gyroscope measurements. The presented scheme leads to a significant computational time-saving method due to considering a pre-filter Kalman for fault detection, hence, the proposed FDD approach consists of two stages. In the first stage, the pre-filter Kalman detects any sensor fault and attitude states. In contrast, in the next stage, fault diagnosis and optimal attitude estimation are obtained through the IMM approach. Therefore, the IMM algorithm will be activated to extract the optimal estimation and identify faulty sensor(s). A Monte Carlo type approach is used to verify this technique for various initial conditions. Through numerical and Monte Carlo simulations, it is shown that the proposed scheme is robust against sensors' total fault, and the system is capable of detecting and isolating accurately the faulty sensor. Hardware in the loop tests using satellite dynamics simulator are also used to validate the presented system performance. [ABSTRACT FROM AUTHOR]

This research paper proposes a new implementation of a long short-term memory network for active fault-tolerant control subjected to single and multiple undiagnosable sensor faults. There are two networks used: the first performs as a diagnosis for automated manufacturing systems and can identify faulty sensors, while the second acts as an inverse model of these systems and is used to determine the reconfigured control action to take when sensors are not functioning as expected. The Factory I/O simulator is interfaced with MATLAB to simulate and verify the proposed approach for automated material handling case study with faultless and multiple fault sensors. Four sensors of the case study out of six are tolerated. [ABSTRACT FROM AUTHOR]

Robust object grasping in cluttered scenes is vital to all robotic prehensile manipulation. In this paper, we present the GraspNet-1Billion benchmark that contains rich real-world captured cluttered scenarios and abundant annotations. This benchmark aims at solving two critical problems for the cluttered scenes parallel-finger grasping: the insufficient real-world training data and the lacking of evaluation benchmark. We first contribute a large-scale grasp pose detection dataset. Two different depth cameras based on structured-light and time-of-flight technologies are adopted. Our dataset contains 97,280 RGB-D images with over one billion grasp poses. In total, 190 cluttered scenes are collected, among which 100 are training set and 90 are for testing. Meanwhile, we build an evaluation system that is general and user-friendly. It directly reports a predicted grasp pose's quality by analytic computation, which is able to evaluate any kind of grasp representation without exhaustively labeling the ground-truth. We further divide the test set into three difficulties to better evaluate algorithms' generalization ability. Our dataset, accessing API and evaluation code, are publicly available at www.graspnet.net. [ABSTRACT FROM AUTHOR]

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the "Giraffe" mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the "Okapi" mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes, demonstrating the potential of combining autonomous driving perception systems with bucket trucks to create a versatile autonomous aerial working platform. Moreover, based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations for multimodal continuous data in both temporal and spatial dimensions. The dataset is available for download at: https://ustc-flicar.github.io/. [ABSTRACT FROM AUTHOR]

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements. [ABSTRACT FROM AUTHOR]

Hot-film sensors have been used to measure the boundary-layer fluctuations and subsequently to determine the boundary-layer states. In this paper, it is proposed to use hot-film sensors as a tool to measure the effects of plasma-induced ionic wind on the boundary-layer fluctuations which can be used to find the influence of the plasma actuator far downstream of the actuator. Time history and frequency response of the hot-film output voltage are investigated to explore the effects of plasma-induced wind downstream of the actuator. Also, the effects of the peak-to-peak voltage of a DBD plasma actuator on the fluctuations of the hot-film output voltages are discussed. It is showed that as far as the effects of ionic wind can be sensed by the hot-films, the control authority of the actuator is higher. It is concluded that induced velocity in the vicinity of the plasma actuator alone does not indicate the control authority of the plasma actuator. [ABSTRACT FROM AUTHOR]

The implantation of nitrogen oxide sensors in diesel engines is necessary in order to track emissions at the engine exhaust line for diagnosis and control of the after-treatment devices. However, the use of models is still necessary since the sensor outputs are delayed and filtered. The present paper deals with the problem of the nitrogen oxide estimation in two parts; Part 1 deals with a control-oriented model for the nitrogen oxide estimation, while Part 2 presents data fusion of the model and the sensor to improve the estimation, which is presented in the following. The use of models for the nitrogen oxide estimation is an alternative but the drift and the ageing are still issues. In order to overcome this problem, the fusion of different signals can be carried out in a smart way by means of a Kalman filter. There exist different ways of presenting this fusion, from directly tracking the bias to updating the model parameters. For this, different algorithms are proposed in this paper with the aim of correcting the model output. Furthermore, the estimation of the actual nitrogen oxide concentration, by preventing sensor delay and filtering, is also integrated in the algorithm, which is a suitable strategy for combining nitrogen oxide sensors and models on an onboard basis. [ABSTRACT FROM AUTHOR]

This paper proposes the use of a two-dimensional refined finite element model, based on the Carrera Unified Formulation (CUF), to predict the variation in capacitance of embedded piezoelectric sensors. The accurate stress field of laminated structures with embedded sensors has been evaluated by exploiting the capabilities of refined layer-wise models. A fully coupled electro-mechanical formulation has been adopted to predict the piezoelectric response of embedded sensors. Different piezoelectric materials have been considered and a parametric analysis has been carried out including geometrical and physical parameters. The results show that the variation in capacitance is highly affected by the through-the-thickness deformations, that is, classical models cannot be used to predict this phenomenon. The variation in the capacitance value has been confirmed by experimental results proposed in the literature and can be used as a parameter for evaluating the internal stress field. [ABSTRACT FROM AUTHOR]

Simulators are valuable tools to evaluate autonomous systems and methods of their control in a cost-effective and safe fashion. There are several implementations of simulators for autonomous systems in the literature. However, they provide limited capabilities in terms of the simulation of specific autonomous methods, for example control, flight, or sensor dynamics. In this paper, we present our design of a simulator for an environment that handles multiple quadrotors with their own set of sensors. We simulate three main sensors to get spatial and environmental information: an Inertial Measurement System that contains a three-axis gyroscope and an accelerometer; a pressure sensor; and ultrasonic sensors. To provide a realistic environment, we implement the thrust force, hub force, total torque, rolling moments, and equations of motion. We provide several attitude controllers, altitude controllers, obstacle detection methods, positioning methods, and flocking methods. We then present a comparison of their performance and behavior. Until the writing of this paper, there were no other simulators that provide this number of various autonomous methods. Moreover, the object-oriented design of our simulator makes it easy to support the addition of any other controller or method. Our simulator provides a comprehensive testbed for current and future methods for autonomous quadrotors. [ABSTRACT FROM AUTHOR]

This study presents an investigation of cellulose as a smart material that can be used for biomimetic sensor/actuator devices and microelectromechanical systems. This cellulose material is termed as electro-active paper (EAPap). First, the fabrication and recent improvement of EAPap materials are addressed. The actuation mechanism is explained by gathering all information on physical, chemical, electrical, and mechanical observations. In addition, the functional capability of sensor/actuator as a new smart material is discussed with experimental testimony. This smart material can be used for many applications, such as micro insect robots, micro flying objects, MEMS, biosensors, and flexible electrical displays. In summary, possibility of cellulose as smart material is addressed with challenges in this research. [ABSTRACT FROM AUTHOR]

Real-time vision-based navigation is a difficult task largely due to the limited optical properties of single cameras that are usually mounted on robots. Multiple camera systems such as polydioptric sensors provide more efficient and precise solutions for autonomous navigation. They are particularly suitable for motion estimation because they allow one to formulate a linear optimization. These sensors capture the visual information in a more complete form called the plenoptic function that encodes the spatial and temporal light radiance of the scene. The polydioptric sensors are rarely used in robotics because they are usually thought to increase the amount of data produced and require more computational power. This paper shows that these cameras provide more accurate estimation results in mobile robotics navigation if designed properly. It also shows that a plenoptic vision sensor with a resolution ranging from 3 × 3 to 40 × 30 pixels camera, provides higher accuracy than a mono-SLAM running on a 320 × 240 pixels camera. The paper also gives a complete scheme to design usable real-time plenoptic cameras for mobile robotics applications by establishing the link between velocity, resolution and motion estimation accuracy. Finally, experiments on a mobile robot are shown allowing for a comparison between optimal plenoptic visual sensors and single high-resolution cameras. The estimation with the plenoptic sensor is more accurate than a monocular high-definition camera with a processing time 100 times lower. [ABSTRACT FROM AUTHOR]

In this paper, a supervisory algorithm for attitude determination sensors of a three-axis satellite is suggested to classify different fault types of this system. The presented solution is based on the computation of rotation matrices between the orbital and body frames. Using the Euler angles provided by these rotations, some variance measures are computed, which utilized as an analytical tool for the determination of faulty sensors and the related fault sources. According to the mentioned idea, it is shown that the fault sources could be categorized into four levels. Each level shows the fault extent and its criticality. To include the reliability aspects in this paper, a failure mode effect criticality analysis is conducted to determine different failure modes, which really occur in sensors. For this, the probability levels and the severity numbers were assigned to derive the critical ones. It is shown that most of the critical failure modes could be isolated by the proposed methods; in other words, the design process is based on real faulty scenarios that can improve the system reliability. Finally, by performing some numerical simulations, performance of the developed algorithms are validated. [ABSTRACT FROM AUTHOR]

The high diversity of urban environments, at both the inter and intra levels, poses challenges for robotics research. Such challenges include discrepancies in urban features between cities and the deterioration of sensor measurements within a city. With such diversity in consideration, this paper aims to provide Light Detection and Ranging (LiDAR) and image data acquired in complex urban environments. In contrast to existing datasets, the presented dataset encapsulates various complex urban features and addresses the major issues of complex urban areas, such as unreliable and sporadic Global Positioning System (GPS) data, multi-lane roads, complex building structures, and the abundance of highly dynamic objects. This paper provides two types of LiDAR sensor data (2D and 3D) as well as navigation sensor data with commercial-level accuracy and high-level accuracy. In addition, two levels of sensor data are provided for the purpose of assisting in the complete validation of algorithms using consumer-grade sensors. A forward-facing stereo camera was utilized to capture visual images of the environment and the position information of the vehicle that was estimated through simultaneous localization mapping (SLAM) are offered as a baseline. This paper presents 3D map data generated by the SLAM algorithm in the LASer (LAS) format for a wide array of research purposes, and a file player and a data viewer have been made available via the Github webpage to allow researchers to conveniently utilize the data in a Robot Operating System (ROS) environment. The provided file player is capable of sequentially publishing large quantities of data, similar to the rosbag player. The dataset in its entirety can be found at http://irap.kaist.ac.kr/dataset. [ABSTRACT FROM AUTHOR]

This paper presents a new fault-tolerant control (FTC) algorithm for an automotive air suspension control (ASC) system. The FTC algorithm proposed in this paper provides the fault detection, diagnosis, and management of a closed-loop air suspension control system. A new model-based fault detection and isolation algorithm for the height sensors, which are critical but vulnerable components of the ASC system, is also proposed. The height sensor fault is detected on the basis of the geometric relationships of the suspension and is isolated by implementing the analytical redundancy of the height sensors using roll and pitch angle estimates derived by a Kalman filter. An adaptive threshold is designed and applied in order to enhance the robustness of the fault detection and isolation against model uncertainties. The effectiveness of the proposed model-based FTC algorithm is verified via simulation and actual vehicle tests. [ABSTRACT FROM AUTHOR]

Piezoelectric wafer active sensors (PWAS) are lightweight and inexpensive enablers for a large class of structural health monitoring (SHM) applications. This paper presents and discusses the challenges and opportunities related to the use of PWAS in the structures specific to space applications. The challenges posed by space structures are often different from those encountered in conventional structures. After a review of PWAS principles, the paper discusses the multi-physics power and energy transduction between structurally guided waves and PWAS; predictive modeling results using a simplified analytical approach are presented. Experimental results on space-like specimen structures are presented. Survivability of PWAS transducers under cryogenic space-like conditions is experimentally verified. The paper ends with conclusions and suggestions for further work. [ABSTRACT FROM PUBLISHER]

This paper presents the results of an investigation on the effects of composite microstructures on the spectral response of fiber Bragg grating (FBG) sensor embedded in smart carbon-epoxy fiber composite, based on the mechanical finite element analysis and optical coupled mode theory. The FBG sensors are embedded in the directions parallel or intersected to the axis of reinforcing carbon tows. It shows that FBG sensors have good linear spectral responses for axial loads in both cases of sensor arrangements, and their sensitivities are close to each other. For lateral compression loads, however, the parallel arrangement has much smaller effects on the spectra than that of intersected arrangement, and thus is proposed for the sensing applications. [ABSTRACT FROM AUTHOR]

Due to the lightweight, inexpensive, and simplicity, coarse sun sensors (CSS) are an interesting part of the small satellites with low-earth orbit. However, the CSS drawbacks such as low accuracy and nonlinear distribution of the error contours through the sensor Field of View (FOV) apply a notable limitation in its application. This forces the literature to focus on laboratory or in-orbit calibration methods. The excellence of the in-orbit calibration in comparison with the laboratory cases is clear; however, the difficulty is to achieve enough data when the satellite is launched. To solve that, in this paper, a conical maneuver based on the Lyapunov theory is designed which causes the sunlight to cover the sensor FOV. After that, using the achieved rich data pack, the CSS calibration process, including the sensor misalignment, solar cells misalignment, and solar cells characteristic curve (Fresnel effect), is accomplished. Moreover, the results show that the combination of the new calibration methods with the iterative least square (ILS) algorithm leads to 0.89° accuracy through the FOV of 120°. [ABSTRACT FROM AUTHOR]

In order to keep wind turbines connected and in operation at all times despite the occurrence of some faults, advanced fault detection and accommodation schemes are required. To achieve this goal, this paper proposes to use the Linear Parameter Varying approach to design an Active Fault Tolerant Control for wind turbines. This Active Fault Tolerant Control is integrated with a Fault Detection and Isolation approach. Fault detection is based on a Linear Parameter Varying interval predictor approach while fault isolation is based on analysing the residual fault signatures. To include fault-tolerance in the control system (already available in the considered wind turbine case study based on the well known SAFEPROCESS benchmark), the information of the Fault Detection and Isolation approach block is exploited and it is used in the implementation of a virtual actuator and sensor scheme. The proposed Active Fault Tolerant Control is evaluated using fault scenarios which are proposed in the wind turbine benchmark to assess its performance. Results show the effectiveness of the proposed Active Fault Tolerant Control approach in faulty situation. [ABSTRACT FROM AUTHOR]

Although most real building structure controllers are in the form of proportional-derivative/proportional-integral-derivative (PD/PID), there have been few published theory results of PD/PID on structural vibration control. In order to minimize the regulation error, a PD/PID control needs relatively large derivative and integral gains. These deteriorate the transient performances of the vibration control. In this paper, a natural combination of industrial PD/PID control with fuzzy compensation is proposed. The main contribution of this paper is that the stability of the fuzzy PD/PID control is proven with standard weight training algorithms. These conditions give explicit selection methods for the gains of the PD/PID control. Experimental studies on a two-story building prototype with the controllers are addressed. The experimental results validate our theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, the multi-position calibration method for low cost micro-electro mechanical systems inertial measurement unit (MEMS IMU) is significantly improved based on two principles. One is that the magnitude of the specific force should be equal to that of the gravity vector. Another is that the gravity vector should be equal to the computed gravity vector which is calculated with the gyroscope outputs. Thus, the error parameters of the MEMS IMU can be estimated without any equipment using the proposed method. Different from previous methods, this paper applied a Kalman filter to calibrate the gyroscopes. This makes the calibration method simple and convenient for the non-technical customers, as the calibration processes can be implemented only by hands. The validity of the method is verified by a real test with a set of low cost MEMS IMU, and the performance is compared with the traditional six-position and rate test methods. The results indicate that the proposed method is effective and efficient for it can be used to calibrate the low cost MEMS IMUs without any special equipment and significantly increase the measurement accuracy. [ABSTRACT FROM AUTHOR]

The synthesis of verifiable robot controllers from a set of high-level task specifications provides a valuable tool for creating robot controllers for complex tasks. Such an approach can offer a number of advantages over more traditional programming methods, including the guarantee that the synthesized controller will satisfy all of its underlying specifications when operating with perfect sensing and actuation. This paper relaxes that assumption, and describes a method for probabilistically analyzing the behavior of a robot controller that is synthesized from a set of temporal logic specifications, when the robot operates with uncertainty in its sensing and actuation. The described approach creates a probabilistic model of the system and uses probabilistic model checking techniques to find the probability that it satisfies some set of specifications. In addition, the paper proposes a method which leverages that analysis to provide automated feedback to the user in the form of suggested revisions to the task specification or low-level components, in order to increase the probability that the robot successfully accomplishes its task. [ABSTRACT FROM PUBLISHER]

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists' ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incorporation of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King's College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it-yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior environmental monitoring at the local to global scales. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in various portable electronic devices, electric vehicles and energy storage systems. The primary challenge in applying a Lithium-ion battery (LiB) system is to guarantee its operation safety under both normal and abnormal operating conditions. To achieve this, temperature management of batteries should be placed as a priority for the purpose of achieving better lifetime performance and preventing thermal failures. In this paper, fibre Bragg Grating (FBG) sensor technology coupling with machine learning (ML) has been explored for battery temperature monitoring. The results based on linear and nonlinear models have confirmed that the novel methods can estimate temperature variations reliably and accurately. [ABSTRACT FROM AUTHOR]

An integrated celestial navigation scheme for spacecrafts based on an optical interferometer and an ultraviolet Earth sensor is presented in this paper. The optical interferometer is adopted to measure the change in inter-star angles due to stellar aberration, which provides information on the velocity of the spacecraft in the plane perpendicular to the direction of the observed star. In order to enhance the navigation performance, the measurements obtained from the ultraviolet Earth sensor is used to eliminate the unfavorable effect caused by the gravitational deflection of starlight. As the prior knowledge about the optical path delay bias of the optical interferometer may be ambiguous, a Q-learning extended Kalman filter is derived to fuse the two types of measurements, and estimate the kinematic state together with the optical path delay bias. The solution of the autonomous navigation system consists of position, velocity and attitude of the spacecraft. Numerical simulation shows that an evident improvement in navigation accuracy can be achieved by introducing the ultraviolet Earth sensor into the navigation system. In addition, it is shown that the Q-learning extended Kalman filter performs better than the traditional extended Kalman filter. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of control of a vehicular platoon system with limited on-board resources in the presence of time-varying delay and sensor faults. A platoon system with predecessor-leader following topology in which each vehicle suffers from probabilistic sensor faults and time-varying delay is considered. To reduce the utilization of communication and energy resources in the system, a novel event-triggering communication strategy at the sensor-controller channel is proposed. Based on the proposed triggering strategy, a criteria for co-designing of the triggering parameter and the control law ensuring internal stability of the platoon system is developed. Furthermore, additional conditions are established for the obtained controller to guarantee string stability of the platoon system. The efficacy of the proposed methodology is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

Accurate and reliable process measurements play a key role in the success of process control. In addition to selecting the instrumentation hardware, it is important to determine which variables should be measured in a process. Therefore it is important to study techniques for placing sensors on the processes. Dually the problem of placing actuators on the processes is equally important. In this paper, the problem of determining optimal sensor and actuator locations for the linear systems is addressed. The problem of the sensor locations is viewed as the problem of maximizing the output energy generated by a given state and for the actuator locations is viewed as the problem of minimizing the input energy required to reach a given state. Such design problems occur in many applications, and therefore have been studied extensively. Unfortunately, the results in this context, which have been proposed so far, only support stable systems. However, in industrial practices it is often the case that the system, which is needed to be controlled, is not stable. The method which is proposed in this paper is a general method in the sense that it supports both stable and unstable systems. The technique is successfully used for determining the optimal sensor locations of the linearized Continuous Stirred-Tank Reactor unstable model. [ABSTRACT FROM AUTHOR]

An appropriate subset of features is needed for a classification-based pedestrian detection system since its performance is greatly affected by the features adopted. Moreover, the combination of different types of features (eg, grey-scale, colour) could improve the detection accuracy, so it is helpful to obtain a feature subset and the proportion of each type simultaneously for the classifier. However, because a larger number and various types of features are generally extracted to represent pedestrians better, it is difficult to achieve this. This paper proposed a co-evolutionary method to solve this problem. In the feature subset selection method, each sub-population mapped to one type of pedestrian feature, and then all sub-populations evolved co-operatively to obtain an optimal feature subset. Moreover, a strategy was specially designed to adjust the sub-population size adaptively in order to improve the optimizing performance. The proposed method has been tested on pedestrian detection applications and the experimental results illustrate its better performance compared with other methods such as genetic algorithm and AdaBoost. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical analysis of model-based fault detection and isolation (FDI) and its application to a motor-driven power steering (MDPS) system. In particular, this paper proposes a systematic approach to determining fault detectability and isolability of a linear time-invariant system based on a parity equation. The proposed approach is applied to the MDPS system in order to examine the fault detectability and isolability of the MDPS depending on the number and type of sensors and disturbances. Fault detection and isolation of the phase current sensors and the angular velocity sensor are conducted using a parity equation derived from the mathematical model of the permanent magnet alternating current (PMAC) motor. The fault of the steering torque sensor is detected and isolated by cross-checking with two different torque estimates. An adaptive threshold is proposed to improve the performance and robustness of the proposed FDI method against time-varying model uncertainties. Simulation and vehicle test results show the validity and performance of the proposed FDI method for the MDPS system. [ABSTRACT FROM AUTHOR]

This paper presents an analytical sensor fault-detection and isolation algorithm for the vertical accelerometers of a continuous damping control (CDC) system, which are essential but vulnerable components in the CDC system. Because the sensor configuration of the target CDC system does not provide sufficient redundancy for fault diagnosis, two different vehicle suspension models, a modified full car model and a roll-and-pitch plane model, are derived and combined to establish analytical redundancy. Parity equations for the vertical motion of a sprung mass are derived using the combined model and applied in order to detect faults of the vertical accelerometers. In order to isolate the faults properly in practical situations, this paper proposes a fault isolation algorithm which calculates estimates of the vertical acceleration at the centre of gravity of the sprung mass using vertical acceleration signals and compares the residuals of these estimates. This paper also provides a detailed analysis regarding the detectability and isolability of vertical accelerometer faults. An adaptive threshold method is proposed to improve the performance of the suggested fault-detection and isolation algorithm. Simulation and test results demonstrate the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper we describe a side slip angle estimation methodology especially for a drive recorder which has limited vehicle sensing information. The drive recorder has attracted the attention of many researchers recently since its captured data contains valuable information that can enhance traffic safety. As the current algorithm of video capture and data acquisition, it detects hard braking by using acceleration sensor values and records the video image and vehicle data in that situation. However, the conventional trigger algorithm cannot be activated and the data cannot be recorded when the vehicle is in a side slip situation on a low friction road with low acceleration. We propose an estimation method of the vehicle side slip angle which uses lateral acceleration, yaw rate and velocity without requiring information about the steering wheel angle for the drive recorder configuration. In this paper we describe a synthesis of the estimation method based on a two-wheel lateral vehicle dynamics model and validation of the proposed methodology by experiments using an actual vehicle. [ABSTRACT FROM AUTHOR]

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient. [ABSTRACT FROM AUTHOR]

Tuning the electromechanical performance in piezoresistive composite strain sensors is primarily attained through appropriately employing the materials system and the fabrication process. High sensitivity along with flexibility in the strain sensing devices needs to be met according to the application (e.g. human motion detection, health and sports monitoring). In this paper, a highly stretchable and sensitive strain sensor with a low-cost fabrication is proposed which is acquired by embedding the chopped carbon fibers sandwiched in between silicone rubber layers. The electrical and mechanical features of the sensor were characterized through stretch/release loading tests where a considerably high sensitivity (the gauge factor about 100) was observed with very low hysteresis. This implies high strain reversibility (i.e. full recovery in each cycle) over 700 loading cycles. Moreover, the sensors exhibited ultra-high stretchability (up to ∼300% elongation) in addition to a low stiffness meaning minimal mechanical effects induced by the sensor for sensitive human motion monitoring applications including large and small deformations. The results suggest the promising capability of the present sensor in reflecting the human body motion detection. [ABSTRACT FROM AUTHOR]

Novel textile moisture sensors based on multi-layered braiding constructions offer attractive monitoring possibilities for many application fields of e-textiles (electronic textiles), especially for ambient air, which is the main focus of this work. In this paper the theoretical foundations and the production, as well as the procedural, mechanical and humidity tests on newly developed, textile-based sensors for moisture measurement are described. The sensor, which is based on a capacitive measurement principle, was tested in environments ranging from 22% to 94% relative humidity. Within this range, the electrical capacitance of the sensors changed up to 135% of the base capacitance, giving it a very high sensitivity. [ABSTRACT FROM AUTHOR]

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use offeature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainly. While the focus of the paper is on task specification, an existing velocity-based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainly coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

In this paper we describe the design, development, and experimental trials of a climbing robot for manufacturing and inspection applications within the aerospace industry. We describe the mechanical platform, which utilizes vacuum for attachment to vertical and overhanging surfaces, and a traction system that enables rapid movement of the robot over planar and curved surfaces of any orientation. The main applications considered during the research were the manufacturing processes for large external surface structures such as wings and the post-manufacturing and in-service inspection of such structures. The design of suitable tool packages for manufacturing and non-destructive testing is considered in the paper. The first tool package to be implemented and tested was a five-axis high precision drill, which is described. The control system is described within this paper along with the software architecture. The software architecture for the robot was generalized, allowing different robot configurations to be described and implemented rapidly through structured configuration files. Particular attention is paid to the robot's localization and navigation system, which provides tool point precision to aircraft manufacturing tolerances. The localization system uses data from several different types of sensors and combines these with information provided by a surface model to derive six-degrees-of freedom position and orientation using an extended Kalman filter to fuse the state information from the different sources. Tool point position is calculated through direct kinematic transformations. Improvements to this work are described, which utilize one of the initial releases of Lieca's new six-degrees-of-freedom precision measurement instruments, the Lieca LTD-800. We also summarize the experimental trials and the initial performance in terms of tool point precision and climbing performance. The concept of application of the robot and details of the technologies included in the robot that are outlined in this paper are the subject of separate pending patent applications filed by BAE SYSTEMS plc. [ABSTRACT FROM AUTHOR]

This paper examines an approach used to design adaptive systems by means of Rapid-Prototyping on the basis of a simulation. The vibration behavior of mechanical systems including actuators, sensors, and an adaptive controller in the time domain will be modeled. This approach includes the calculation of the dynamic behavior of the mechanical structure with the help of the Finite Element Method (FEM). The FE model of the mechanical structure is transformed into modal space, then reduced and embedded as a state-space model in the MATLAB/Simulink environment. Thereby, a simple mechanical problem is solved analytically with the FEM as well as with the reduced MATLAB model. The comparison of the results shows a good agreement. Actuators and sensors are attached to the mechanical structure. Based on the resulting dynamical behavior of the mechanical structure, an adaptive control algorithm for the reduction of structural vibration is developed. Experimental tests are performed to verify and update the simulations. The hardware-in-the-loop simulations are carried out with a dSpace system. Some differences (with respect to speed-up, precision) between this simulation and other methods are presented later in the paper. This procedure allows the development and the evaluation of more complex adaptive mechanical structures. [ABSTRACT FROM AUTHOR]

Among fiber-optic distributed sensors, Brillouin optical time domain reflectometry (BOTDR) can effectively measure overall strain in a structure. However, since the spatial resolution of BOTDR is generally more than 1 m, it is difficult to detect inhomogeneous strain distributed within 1m along a fiber. In this paper, we propose a new technique to detect strain changing sharply within the length of the spatial resolution. This technique is based on the fact that the profile of the Brillouin spectrum changes depending on strain distributions. We confirmed the dependency of the Brillouin spectrum on strain distributions theoretically and experimentally. [ABSTRACT FROM AUTHOR]

In this paper, the filtering problem is investigated for nonlinear systems with multiple sensors. A federated strong tracking filter is designed to track state mutations by making full use of limited sensor information. Several subsystems are composed of different sensor combinations, and their states are independently estimated by using local filters in parallel. The strong tracking filter, as local filters, adaptively adjusts gain matrices of filters by introducing fading factors to track state mutations timely. Based on the theory of boundedness and inequality technique, the fusion estimation error is proved to be exponentially bounded in mean square. Finally, the feasibility and effectiveness of the proposed method is demonstrated by an experiment concerning the rotary steering drilling tool system. [ABSTRACT FROM AUTHOR]

Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable; however, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units, rolling-shutter cameras, or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full Maximum-a-Posteriori estimation problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian and show how the coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms through the application of different assumptions at any point. We use the simultaneous localization and mapping problem as our motivation throughout the paper, although the approach is not specific to this application. Results from two experiments are provided to validate the approach: (i) self-calibration involving a camera and a high-rate inertial measurement unit, and (ii) perspective localization with a rolling-shutter camera. [ABSTRACT FROM AUTHOR]

Mobile robots are undergoing tremendous development, which makes them employed in many fields. In this area, global localization in symmetric indoor environments is a commonly encountered problem. One of the commonly used algorithms to solve it, is the Adaptive Monte Carlo Localization (AMCL), which is based on the particle filter algorithm. In this paper, we developed a new algorithm for resampling used within the Adaptive Monte Carlo Localization (AMCL) framework that we called Effective Samples Resampling (ESR). The proposed algorithm is based on a deterministic sample selection, and it is thoroughly tested in real time. Using a considerable amount of simulations, the efficacy and robustness of the AMCL using this technique are validated and compared to certain conventional approaches. They are also tested and validated in various real-time operating conditions using the Robot Operating System (ROS). The obtained results are quite satisfying in terms of resampling quality, implementation complexity, and convergence time when compared to random resampling approaches where a sample-based probability density given by high-quality sensors might destabilize localization. The global localization is well handled when the proposed algorithm is involved, compared to standard resampling algorithms that can often be overconfident and fail in some scenarios when there is a lot of symmetry in the considered map of the environment. [ABSTRACT FROM AUTHOR]

Considering that existing theories consider runway lights as markers, neglecting their roles as lighting devices that increase sky background luminance and thus affect runway visual range (RVR) measurements, the actual values of sky background luminance observed by pilots during take-off are calculated and compared with the instrumental RVR measurements in this paper. The RVR measurement errors due to the installation positions of these devices are subsequently analysed. Furthermore, the impacts of different runway light types and positions on sky background luminance are explored, leading to the proposal of an optimized runway lighting scheme. The results show that the representation of sky background luminance sensor data in RVR measurement systems for nighttime airport runways is limited. A deviation of over 90% exists between the sky background luminance of the runway entrance and the RVR measurement equipment location, resulting in a relative error in RVR measurements of over 18%. The centreline and entrance lights contribute significantly to the total sky background luminance. Lights closer to the observer contribute more luminance. In the second proposed scheme, modifying the lighting range to 600 m yields the highest RVR improvement, with an optimization magnitude of over 5% across all visibility ranges. [ABSTRACT FROM AUTHOR]

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model. [ABSTRACT FROM AUTHOR]