Downloads & Free Reading Options - Results

Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. The proposed method uses joint torque sensors found in most existing advanced robot designs along with easily locatable, lightweight accelerometers to provide a joint position sensor fault recovery mode. This mode uses the torque sensors along with a virtual passive control law for stability and accelerometers for joint position information. Two methods for conversion from Cartesian acceleration to joint position based on robot kinematics, not integration, are presented. The fault tolerant control method was tested on several joints of a laboratory robot. The controllers performed well with noisy, biased data and a model with uncertain parameters.

This report covers the development of a three channel Hall effect position sensing system for the commutation of a three phase dc torquer motor. The effort consisted of the evaluation, modification and re-packaging of a commercial position sensor and the design of a target configuration unique to this application. The resulting design meets the contract requirements and, furthermore, the test results indicate not only the practicality and versatility of the design, but also that there may be higher limits of resolution and accuracy achievable.

This report provides a procedure for estimating a contact's course, speed and position based on bearings-only data from two moving sensors. This report also contains a program for the HP-67/97 calculator to implement the procedure. (Author)

In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

This work derives the Cramer-Rao lower bound (CRLB) for an acoustic target and sensor localization system in which the noise characteristics depend on the location of the source. The system itself has been previously examined, but without deriving the CRLB and showing the statistical efficiency of the estimator used. Two different versions of the CRLB are derived, one in which direction of arrival (DOA) and range measurements are available ( full-position CRLB ), and one in which only DOA measurements are available ( bearing-only CRLB ). In both cases, the estimator is found to be statistically efficient; but, depending on the sensor-target geometry, the range measurements may or may not significantly contribute to the accuracy of target localization.

Simple straight brio railway with holes to mount sensors to detect the train passing the rail as demonstrated here: https://youtu.be/1tibVwowwuU My other Brio Railway parts: https://www.thingiverse.com/ascii_ch/collections/brio-railway

The performance of three types of normal shock position sensors for supersonic inlets is described. All three sensors determined the shock position from the presence of the large pressure gradient at the normal shock location. The logic means for the three sensors were: (1) electronic, using pressure transducers, (2) fluidic, and (3) direct-coupled pressure switches. The sensors were evaluated in a two-dimensional, Mach 2.7, supersonic inlet having 30-percent internal supersonic area contraction. Both dynamic (from 1 to 80 Hz) and static tests were conducted.

This article is from Sensors (Basel, Switzerland) , volume 9 . Abstract Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated.

Navigation of gun-launched precision munitions using affordable technologies is investigated. Estimation algorithms were developed to blend flight dynamic models with measurements from inertial sensors and, if available, global positioning system (GPS). The launch and flight characteristics of the unique, gun-launched environment were exploited both in the state estimator and for novel heuristic parameter identification. The algorithm included using low-cost inertial sensor arrays and general GPS availability. Experimental results from guided mortar flights indicate that the algorithm with only inertial sensor measurements yields position errors less than 40 m over a 30-s flight. Position errors from the experiments decrease slightly using loose coupling when GPS is available. Simulations were conducted to assess algorithm performance over a wider range of conditions. These results demonstrate that position errors are less than tens of meters for flight times of interest to munitions. Estimation is intolerant to inertial sensor errors due to the novel manner in which known flight dynamics are used to compensate measurements. Overall, this effort shows that navigation error resulting from a low throughput algorithm using affordable inertial sensors is sufficient to increase system accuracy for munitions

Navigation of gun-launched precision munitions using affordable technologies is investigated. Estimation algorithms were developed to blend flight dynamic models with measurements from inertial sensors and, if available, global positioning system (GPS). The launch and flight characteristics of the unique, gun-launched environment were exploited both in the state estimator and for novel heuristic parameter identification. The algorithm included using low-cost inertial sensor arrays and general GPS availability. Experimental results from guided mortar flights indicate that the algorithm with only inertial sensor measurements yields position errors less than 40 m over a 30-s flight. Position errors from the experiments decrease slightly using loose coupling when GPS is available. Simulations were conducted to assess algorithm performance over a wider range of conditions. These results demonstrate that position errors are less than tens of meters for flight times of interest to munitions. Estimation is intolerant to inertial sensor errors due to the novel manner in which known flight dynamics are used to compensate measurements. Overall, this effort shows that navigation error resulting from a low throughput algorithm using affordable inertial sensors is sufficient to increase system accuracy for munitions

The objective of this thesis was to further the development of a personal position tracking system using MARG sensors. This work advanced the method by which distance and heading were calculated of an individual wearing one MARG sensor on his/her foot when moving about under normal walking conditions. Data was collected from the foot-mounted sensor while walking a straight-line path, a square path, and climbing stairs. The corresponding data from these activities was then used in a Matlab program to determine a computed position. The Matlab program employed a technique that reset the accelerometer error during the stance phase of the gait cycle. It also utilized a gait detection algorithm based on the magnitude of angular rate and the number of samples above/below threshold to establish the periods of the stance phase and the swing phase. Experimental results from various testing scenarios showed that it is feasible to track position of a person.

The usefulness of remotely controlled manipulators is increasing as the need grows to accomplish complex tasks in hazardous environments such as the deep ocean. The best sensory input currently available to the operator of a remote supervisory controlled manipulator is a television picture of the manipulator and its surroundings. Very often, though, optical opacity due to suspended particles in the water can make television impractical or impossible to use. This report investigates the use of touch sensors to construct a picture of the manipulator surroundings. One method studied was to find 3-dimensional surface points and show them on a computer graphic display. An extension of this was to reconstruct the surface of these points with the aid of a computer.

Eddy current position sensor performance is evaluated for use in a high-speed flywheel development system. The flywheel utilizes a five axis active magnetic bearing system. The eddy current sensors are used for position feedback for the bearing controller. Measured characteristics include sensitivity to multiple target materials and susceptibility to noise from the magnetic bearings and from sensor-to-sensor crosstalk. Improvements in axial sensor configuration and techniques for noise reduction are described.

Two optical sensors, a 360 deg rotary encoder and a tachometer, were built for operation with the light source and detectors located remotely from the sensors. The source and detectors were coupled to the passive sensing heads through 3.65 meter fiber optic cables. The rotary encoder and tachometer were subjected to limited environmental testing. They were installed on an air breathing engine during recent altitude tests. Over 100 hours of engine operation were accumulated without any failure of either device.

Two optical sensors, a 360 deg rotary encoder and a tachometer, were built for operation with the light source and detectors located remotely from the sensors. The source and detectors were coupled to the passive sensing heads through 3.65 meter fiber optic cables. The rotary encoder and tachometer were subjected to limited environmental testing. They were installed on an air breathing engine during recent altitude tests. Over 100 hours of engine operation were accumulated without any failure of either device.

Title from cover

Title from cover

This article is from Sensors (Basel, Switzerland) , volume 11 . Abstract Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method.

A test was conducted to determine the performance of a variable reluctance proximity sensor system when installed in a space shuttle external tank vent/relief valve. The sensors were used as position indicators. The valve and sensors were cycled through a series of thermal transients; while the valve was being opened and closed pneumatically, the sensor's performance was being monitored. During these thermal transients, the vent valve was cooled ten times by liquid nitrogen and two times by liquid hydrogen. It was concluded that the sensors were acceptable replacements for the existing mechanical switches. However, the sensors need a mechanical override for the target similar to what is presently used with the mechanical switches. This override could insure contact between sensor and target and eliminate any problems of actuation gap growth caused by thermal gradients.

Cryogenic liquids, particularly liquid xenon and argon, are of interest as detector media for experiments in nuclear and particle physics. Here we present a new detector diagnostic technique using piezoelectric sensors to detect bubbling of the liquid. Bubbling can indicate locations of excess heat dissipation e.g., in immersed electronics. They can also interfere with normal event evolution by scattering of light or by interrupting the drift of ionization charge. In our test apparatus, four sensors are placed in the vacuum space of a double-walled dewar of liquid nitrogen and used to detect and locate a source of bubbling inside the liquid volume. Utilizing the differences in transmitted frequencies through the different media present in the experiment, we find that sound traveling in a direct path from the source to the sensor can be isolated with appropriate filtering. The location of the source is then reconstructed using the time difference of arrivals (TDOA) information. The reconstruction algorithm is shown to have a 95.8% convergence rate and reconstructed positions are self-consistent to an average +/-0.5cm around the mean in x, y, and z. Systematic effects are observed to cause errors in reconstruction when bubbles occur very close to the surfaces of the liquid volume.

Experimental data on the errors introduced in wall shear stress measurements when flush-mounted hot-film sensors are not properly positioned in relation to the test surface are presented. These data lead to the establishment of acceptable tolerance limits when positioning sensors in a calibration facility and also in the subsequent relocation of the sensor to where actual measurements are to be taken. Consideration is given not only to errors in mean values of wall shear stress, but also to RMS and instantaneous values. Results show that the hot-film sensor must be positioned within close tolerances for accurate shear stress measurements. Even though all testing was conducted on a 5 cm diameter pipe with fully developed turbulent flow, results are also applicable to other flows such as that over flat plates and in pipes of larger diameters.

Experimental data on the errors introduced in wall shear stress measurements when flush-mounted hot-film sensors are not properly positioned in relation to the test surface are presented. These data lead to the establishment of acceptable tolerance limits when positioning sensors in a calibration facility and also in the subsequent relocation of the sensor to where actual measurements are to be taken. Consideration is given not only to errors in mean values of wall shear stress, but also to RMS and instantaneous values. Results show that the hot-film sensor must be positioned within close tolerances for accurate shear stress measurements. Even though all testing was conducted on a 5 cm diameter pipe with fully developed turbulent flow, results are also applicable to other flows such as that over flat plates and in pipes of larger diameters.

Experimental data on the errors introduced in wall shear stress measurements when flush-mounted hot-film sensors are not properly positioned in relation to the test surface are presented. These data lead to the establishment of acceptable tolerance limits when positioning sensors in a calibration facility and also in the subsequent relocation of the sensor to where actual measurements are to be taken. Consideration is given not only to errors in mean values of wall shear stress, but also to RMS and instantaneous values. Results show that the hot-film sensor must be positioned within close tolerances for accurate shear stress measurements. Even though all testing was conducted on a 5 cm diameter pipe with fully developed turbulent flow, results are also applicable to other flows such as that over flat plates and in pipes of larger diameters.

Experimental data on the errors introduced in wall shear stress measurements when flush-mounted hot-film sensors are not properly positioned in relation to the test surface are presented. These data lead to the establishment of acceptable tolerance limits when positioning sensors in a calibration facility and also in the subsequent relocation of the sensor to where actual measurements are to be taken. Consideration is given not only to errors in mean values of wall shear stress, but also to RMS and instantaneous values. Results show that the hot-film sensor must be positioned within close tolerances for accurate shear stress measurements. Even though all testing was conducted on a 5 cm diameter pipe with fully developed turbulent flow, results are also applicable to other flows such as that over flat plates and in pipes of larger diameters.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

The objective of this thesis was to further the development of a personal position tracking system using MARG sensors. This work advanced the method by which distance and heading were calculated of an individual wearing one MARG sensor on his/her foot when moving about under normal walking conditions. Data was collected from the foot-mounted sensor while walking a straight-line path, a square path, and climbing stairs. The corresponding data from these activities was then used in a Matlab program to determine a computed position. The Matlab program employed a technique that reset the accelerometer error during the stance phase of the gait cycle. It also utilized a gait detection algorithm based on the magnitude of angular rate and the number of samples above/below threshold to establish the periods of the stance phase and the swing phase. Experimental results from various testing scenarios showed that it is feasible to track position of a person.

This article is from Sensors (Basel, Switzerland) , volume 14 . Abstract Conoscopic holography (CH) is a non-contact interferometric technique used for surface digitization which presents several advantages over other optical techniques such as laser triangulation. Among others, the ability for the reconstruction of high-sloped surfaces stands out, and so does its lower dependence on surface optical properties. Nevertheless, similarly to other optical systems, adjustment of CH sensors requires an adequate selection of configuration parameters for ensuring a high quality surface digitizing. This should be done on a surface located as close as possible to the stand-off distance by tuning frequency (F) and power (P) until the quality indicators Signal-to-Noise Ratio (SNR) and signal envelope (Total) meet proper values. However, not all the points of an actual surface are located at the stand-off distance, but they could be located throughout the whole working range (WR). Thus, the quality of a digitized surface may not be uniform. The present work analyses how the quality of a reconstructed surface is affected by its relative position within the WR under different combinations of the parameters F and P. Experiments have been conducted on AISI 316 wire EDM machined flat surfaces. The number of high-quality points digitized as well as distance measurements between different surfaces throughout the WR allowed for comparing the metrological behaviour of the CH sensor with respect to a touch probe (TP) on a CMM.

Two simple position or displacement sensors based on inductance-capacitance resonant circuits have been conceived. These sensors are both powered and interrogated without use of wires and without making contact with other objects. Instead, excitation and interrogation are accomplished by means of a magnetic-field-response recorder. Both of the present position or displacement sensors consist essentially of variable rectangular parallel-plate capacitors electrically connected in series with fixed inductors. Simple inductance-capacitance circuits of the type used in these sensors are inherently robust; their basic mode of operation does not depend on maintenance of specific environmental conditions. Hence, these sensors can be used under such harsh conditions as cryogenic temperatures, high pressures, and radioactivity.