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To describe the architecture of iPOS (short for iPAQ positioning system), a novel fault‐tolerant and adaptive self‐positioning system with quality‐of‐service (QoS) guarantees for resource‐limited mobile devices.

The iPOS architecture is based on a novel sensor modelling technique in combination with a probabilistic data‐fusion engine, which is capable of efficiently combining the location information obtained from an arbitrary number of heterogeneous location sensors. As a proof of concept, the paper present a prototypical implementation for handheld devices, which was evaluated by means of practical experiments.

A major advantage of the iPOS positioning system is its extensibility and flexibility, which is achieved by means of an open plugin architecture and the support of global positioning coordinates according to the WGS‐84 standard. The iPOS system scales very well with respect to the number of sensor plugins that can be operated in parallel. The main limiting factor for the number of supported active plugins is the amount of available system resources on the MoD. With regard to recognition, the experimental results indicate a good accuracy of the fusion‐based positioning system in comparison to the accuracy of the individual sensing technologies. Thanks to the explicit modelling of reliable sensor events, the iPOS system is capable of providing QoS guarantees to applications with regard to the achieved positioning accuracy.

During the experiments, the author recognized time synchronisation as an important challenge that should be addressed as part of future work.

The system enables resource‐restricted mobile devices and computerised objects to exploit computing resources found in their immediate physical vicinity (locality).

The paper presents a novel lightweight sensor‐fusion architecture for fault‐tolerant and adaptive self‐positioning that performs well on resource‐limited mobile devices. A special feature of the developed data‐fusion architecture is the application of a novel event modelling technique that enables the positioning system to give QoS guarantees under certain conditions.

Sensors considerably increase the new applications of industrial robots. Assembly utilizations exist in the fields of part loading, the supervision of the gripping and part mating process, and tolerance compensation as well as the testing and checking of the assembly job. A couple of years ago sensor applications in assembly with industrial robots existed only in research laboratories and development institutions. Nowadays, industry cannot refuse to use them. Improvements on sensorics such as faster data processing and lower costs, as well as higher accuracies in measuring and a more favourable rate to price and efficiency, allow sensor guided robots in assembly with technical and economical significance. Assembly systems with industrial robots demand high flexibility, process supervision and control to increase output quality, and require manifold sensor utilizations.

The tactile sensor with array structure normally has the defects of existing nondetection zone, complex and nonstretchable structure. It is difficult to seamlessly attach to the surface of the robot. For this reason, this paper proposes a method to prepare nonarray structure tactile sensor directly on the surface of the robot by spraying process.

Based on the principle of gradient potential distribution, the potential fields are constructed in two different directions over the conductive film in time-sharing. The potentials at touching position in the two directions are detected to determine the coordinate of the touching point. The designed tactile sensor based on this principle consists of only three layers. Its bottom layer is designed as a weak conductive film made of graphite coating and used to construct the potential field. It can be sprayed either on PET substrate or directly on robot surface.

The radial basis function neural network is used for remodeling the potential distribution, which can effectively solve the problem of nonlinear potential distribution caused by irregular sensor shape, and uneven conductivity at different points of the spraying coating. The simulation and experimental results show that the principle of the proposed tactile sensor used for touching position detection is feasible to be applied to complex surfaces of the robot.

This paper proposed a nonarray customizable tactile sensor based on the spraying process. The sensor has a simple structure, and only five lead wires are needed to realize the coordinate detection of the touch position.

This paper aims to present a simulation concept and an experimental verification of a novel sensor design for the shaft position measurement based on the eddy current principle and the phase-shift measurement. The simulation method for the sensor characteristic determination is presented. Possible application of a new sensor type is theoretically presented, verified in simulation and compared with experimental results.

Sensor is based on the injection-locking phenomenon between coupled oscillators. Only one sensor per axis is used for position measurement. A pair of the sensing and reference oscillators in the sensor is electrically coupled via the coupling resistor. A change in the inductance for the eddy current sensor is simulated in the finite element method (FEM) software Flux and behavior of the sensor circuit is simulated in the SPICE simulator software LTSpice program. Finally, the simulation results are compared with the measurements conducted on the laboratory test rig.

A novelty in this approach is the usage of only one sensor per axis compared to the well-known differential measurement of the position that uses the opposite pair of the sensing oscillators in the same axis. A methodology for the sensor characteristic determination is presented and experimentally verified.

A new variation of a coupled-oscillator eddy current sensor design is introduced. A simulation approach for the characteristic determination of the sensors based on the weakly coupled oscillators and the injection-locking mechanism is presented.

The brake controller is a key component of the locomotive brake system. It is essential to study its safety.

This paper summarizes and analyzes typical faults of the brake controller, and proposes four categories of faults: position sensor faults, microswitch faults, mechanical faults and communication faults. Suggestions and methods for improving the safety of the brake controller are also presented.

In this paper, a self-judgment and self-learning dynamic calibration method is proposed, which integrates the linear error of the sensor and the manufacturing and assembly errors of the brake controller to solve the output drift. This paper also proposes a logic for diagnosing and handling microswitch faults. Suggestions are proposed for other faults of brake controller.

The methods proposed in this paper can greatly improve the usability of the brake controller and reduce the failure rate.

The purpose of this paper is to present a simulation study using a model of a new optically pumped magnetometer sensor for application in the field of magnetoencephalography. The effects of sensor distance and orientation on the measurement information and the sensitivity to neuronal sources are investigated. Further, this paper uses a combinatorial optimization approach for sensor placement to measure spontaneous activity in the region of the occipital cortex.

This paper studies the effects of sensor distance and orientation on sensitivity to cortical sources and measurement information. A three-compartment model of the head, using the boundary element method, is applied. For sensor setup optimization, a combinatorial optimization scheme is developed.

The sensor distance to sources considerably affects the sensitivity and the retrieved information. A specific arrangement of four sensors for measuring spontaneous activity over the occipital part of the head is optimized by effectively avoiding position conflicts.

Individual head models, as well as more detailed noise and signal models, will increase the significance for specific-use cases in future studies.

Effects of sensor distance and orientation are specifically evaluated for a new optically pumped magnetometer. A discrete optimization scheme for sensor optimization is introduced. The presented methodology is applicable for other sensor characterization and optimization problems. The findings contribute significantly to the development of new sensors.

The aim of the research is to achieve a robot skin which is easy to use, and can detect both position and force interacted between robot and environments.

The new type of robot skin proposed in this paper includes two functional modules – contact position sensor and contact force sensor. The contact position sensor module is based on the resistor divider principle, which consists of two perpendicular conductive fiber layers and insulated dot spacer between them. The contact force sensor module is based on capacitance change theory, which consists of two soft conductive plates and a viscoelastic layer between them. By combining the two modules, the soft robot skin was designed.

Simulation and experiment results demonstrate that the proposed robot skin design is feasible and effective enough to sense contact position and contact force simultaneously.

This robot skin is low-cost and easy to make and use, which provides safety solutions for most of the robot.

For the first time, an integrated robot skin which can get contact position and force information simultaneously is designed. Unlike general tactile sensor matrices, this robot skin has only six leads. Furthermore, the number of leads does not increase with the enlarging of sensor area. Soft and simple structure of the robot skin makes it possible to cover any region of the robot body.

The purpose of this paper is to describe a system that provides the driver or the driving assistance system with the lateral position information of the vehicle on the lane in order to detect as early as possible run‐off‐road and then prevent a foreseeable crash.

A magnetized tape deposited on the road in the middle of the lane generates a magnetic field which is detected by on‐board sensors. Depending on the distance between the tape and the sensors, accurate positioning information can be estimated.

The use of at least five sensors makes it possible to obtain a 2 mm‐lateral‐positioning accuracy.

Magnetized tapes are relatively cheap to install and provide an accurate vehicle lateral positioning with low ‐cost magneto‐resistance on‐board sensors. In addition , a magnetization variation along the tape would allow information to be coded which could be then transmitted to the driver.

This paper aims to introduce an aircraft engine inspection robot (AEIR) which can go in the internal of the aircraft engine without collision and detect damage for engine blades.

To obtain the position and pose information of the blades inside the engine, a novel tactile sensor based on electrical impedance tomography (EIT) is developed, which could provide location and direction information when it contacts with an unknown object. In addition, to navigate the continuum robot, a control method is proposed to control the continuum robot, which can control the continuum robot to move along the pre-planned path and reduce the deviation from the planned path.

Experiment results show that the average error of contact location measurement of the tactile sensor is 0.8 mm. The average error relative to the size (diameter of 18 mm) of the sensor is 4.4%. The continuum robot can successfully reach the target position through a gap of 30 mm and realize the spatial positioning of blades. The validity of the AEIR for engine internal blade detection is verified.

The aero-engine inspection robot developed in this paper can replace human to detect engine blades and complete different detection tasks with different kinds of sensors.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.