Position Estimation Error Research Articles

Robotic Swarm Motion Planning for Load Carrying and Manipulating

Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of $N\ge 6$ agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model's full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios.

Single-Anchor Positioning: Multipath Processing With Non-Coherent Directional Measurements

High-accuracy indoor radio positioning can be achieved by using (ultra) wideband (UWB) radio signals. Multiple fixed anchor nodes are needed to compute the position or alternatively, specular multipath components (SMCs) extracted from radio signals can be exploited. In this work, we study a multipath-based, single-anchor positioning system that acquires directional measurements non-coherently. These non-coherent measurements can be obtained, e.g., from a single-chain mm-wave transceiver with analog beam steering or from a low-complexity ultra-wideband transceiver with switched directional antennas. The directional antennas support the separation of SMCs and the suppression of the undesired diffuse multipath component (DMC) with the benefit that the required signal bandwidth can be drastically reduced. The paper analyzes the Cramer-Rao lower bound (CRLB) on the position estimation error to gain insight in the influence of the system design parameters as well as the impact of the DMC on the position error. The CRLB is compared between the non-coherent antenna setup, a conventional array with coherent processing, and a single-antenna setup. A maximum-likelihood position estimation algorithm is formulated. Its performance is evaluated with synthetically generated data as well as with UWB measurements. We show that the accuracy and robustness are significantly improved due to the processing of angular information. Analyzing the measured data for a line-of-sight link, the median error decreases from 22 down to 7 cm, the measurements better than 20 cm increase from 46 to 95 %, and outliers above 50 cm reduce from 12 to 0%.

Dual Quasi-Resonant Controller Position Observer Based on High Frequency Pulse Voltage Injection Method

This paper proposed a dual quasi-resonant controller position observer for conventional pulsating high frequency voltage injection method. The proposed position observer can not only improve the dynamic performance of the sensorless control, but can also compensate the position error fluctuation caused by the dead-time effect. To improve the dynamic performance, the digital bandpass filter in the traditional position observer used to extract high frequency current response is replaced by a quasi-resonant controller firstly. Moreover, an improved Luenberger observer without lowpass filter, which is usually used in traditional position observer to filter the noise in speed information, is adopted in the new position observer. Therefore, dynamic performances can be improved. Then, to reduce the sixth harmonic in the magnitude of position error and speed error caused by the dead-time effect, a frequency adaptive quasi-resonant controller is connected in parallel with the proportional-integral controller in the Luenberger observer. The experiment results verify that the proposed observer can reduce the position estimation error not only in steady state operation conditions, but in variable speed and variable load conditions, and the speed variation range can be widened as well.

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

The real-time response and accuracy of the attitude (i.e., roll and pitch) estimation from low-cost inertial measurement unit (IMU) have become the key issues restricting related applications. This paper proposes a robust attitude estimation scheme which can perform well under dynamic conditions. When only accelerometers are used to calculate and correct the attitude, the external acceleration becomes the main source of attitude estimation errors. Moreover, the truncation error in the linearization process of the nonlinear system also affects the attitude estimation. As our first contribution, the external acceleration is modeled as a first-order Gauss Markov model, and its value is calculated under the indirect Kalman filter (IKF) framework. The measurement noise covariance matrix of the IKF is adaptively adjusted to enhance its robustness and reduce the negative impact caused by inaccurate modeling. In the second part of our work, the two-step cascade filter method is used for attitude estimation. The attitude obtained from the gravity field based on the gradient descent (GD) algorithm shows fast response capabilities, and hence, it is embedded as a measurement in the IKF by using the chain-derivation rule. The truncation error introduced into the linearization process of the nonlinear system is effectively avoided. Both simulation and experiments are carried out to verify the feasibility and accuracy of the proposed algorithm. The results show that the approach proposed in this paper can meet the accuracy requirements of consumer products.

Sensorless Synchronous Reluctance Motor Drives: A Full-Speed Scheme using Finite-Control-Set MPC in a Projection Vector Framework

The article presents a general framework valid for the high-frequency excitation-based position observer for low-speed sensorless control of synchronous reluctance machines. A finite-control-set model-predictive-control technique is proposed accordingly, exploiting the switching current ripple for the position estimation. The position error due to cross saturation is inherently accounted for. The proposed scheme is integrated with an adaptive projection vector for position error estimation for high speeds region through a simple fusion structure for a smooth transition. The performance of the proposed technique is validated on a 1.1-kW synchronous reluctance motor test bench.

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

Human movement effects on the performance of the RSSI-based trilateration method: adaptive filters for distance compensation

In this paper, an experimental study of human movement effects in indoor wireless networks on the performance of the received signal strength indicator (RSSI)-based trilateration method is presented. The contribution of this work is that how the RSSI fluctuation caused by human movements nearby a target node influences the estimation accuracy of the trilateration method is studied. Additionally, efficient adaptive filters which can observe RSSI variation levels and select appropriate inputs (i.e., distance values converted from RSSI values) are proposed for handling the RSSI variation problem and compensating the position estimation error. Experiments using low-cost 2.4-GHz wireless nodes have been set and tested in a laboratory room. Results demonstrate that, during the human blocking the communication link between the transmitter and the receiver, RSSI data are fluctuated and the trilateration method shows a large estimation error. Here, the localization accuracy significantly depends on the RSSI variation level. The experimental results also indicate that, by applying our proposed solutions, they can automatically provide appropriate inputs for the trilateration method. The estimation error decreases by 43.76% and 46.28% for our test scenarios.

Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images

Flexible needles have been widely used in minimally invasive surgeries, especially in percutaneous interventions. Among the interventions, tip position of the curved needle is very important, since it directly affects the success of the surgeries. In this paper, we present a method to estimate the tip position of a long-curved needle by using 2D transverse ultrasound images from a robotic ultrasound system. Ultrasound is first used to detect the cross section of long-flexible needle. A new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. After that, the needle shape tracking method is proposed by combining the image processing with the Kalman filter by using 3D needle positions, which develop a robust needle tracking procedure from 1 mm to 8 mm scan intervals. Shape reconstruction is then achieved using the curve fitting method. Finally, the needle tip position is estimated based on the curve fitting result. Experimental results showed that the estimation error of tip position is less than 1 mm within 4 mm scan intervals. The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle’s cross sections at intervals along the direction of needle insertion without detecting the tip.

High-Performance Selective and Output Filter Techniques for Sensorless Direct Position and Speed Estimation

This article introduces high-performance filter techniques for direct position and speed estimation to augment its robustness against disturbances. The direct estimation concept provides an independent position and speed estimate at each sampling instant by solving an optimization problem parameterized with the current, current derivative, and voltage of the same sample. It can operate at any speed employing a voltage injection at low speed or pulsewidth modulated current derivative. A selective filter concept is proposed that discards samples lacking robustness based on cost function properties. The concept is most effective in removing worst case errors and should always be applied. Also, output filter techniques are investigated to improve the estimates. A finite impulse response (FIR) structure is proposed that filters estimates according to a least-square criterion and is effective in reducing average estimation errors. The FIR filter is benchmarked against an enhanced dual phase-locked loop (PLL) filter, which is enabled by direct estimation. The FIR and dual-PLL filters are found to have a 6.8 and 1 kHz practical bandwidth, respectively, while achieving a $ 1% absolute mean position and speed estimation error. Hence, they perform one to two orders of magnitude better than traditional estimation schemes, which typically achieve $ 100 Hz bandwidth at similar errors.

User fairness for RSS‐based positioning in uplink cooperative NOMA

Geolocation of mobile nodes with non-orthogonal multiple access (NOMA) is a crucial process for enabling advanced location-based services (LBSs) in 5G networks. In this study, the authors investigate the theoretical limits of received-signal-strength (RSS)-based positioning for uplink cooperative NOMA under different network conditions with special emphasis on the user fairness feature. In addition, they compare the performance of the RSS-based positioning of cooperative NOMA with that of orthogonal multiple access (OMA). The Cramer-Rao lower bounds are derived in order to show the fundamental limits of position estimation error by taking amplify-and-forward and decode-and-forward relaying into account. The numerical results for both schemes are presented and compared considering the effects of number of relays and user equipments, different relaying on the performance of RSS positioning. The effects of power allocation on the user fairness of cooperative NOMA are also investigated. The results show that the cooperative NOMA outperforms cooperative OMA in terms of positioning. It provides high precision position accuracy and user fairness under different network conditions. Hence, the results suggest that the RSS-based positioning for uplink cooperative NOMA is a promising technique for achieving user fairness and supporting diversified low-cost LBSs in 5G networks.

Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation

AbstractReceived Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance‐based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons‐based indoor positioning systems provide an easy and energy‐efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in‐depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real‐time experimental analysis using BLE modules, there is an average 1.32‐m position estimation error in the presence of 10‐dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

Correction of Rotor Position Estimation Error for High-Speed Permanent Magnet Synchronous Motor Sensorless Drive System Based on Minimum-Current-Tracking Method

This article focuses on the issue of the rotor position estimation error correction for high-speed permanent magnet synchronous motor sensorless drive system. First, a rotor position estimator is designed. Then the estimation error caused by different kinds of nonideal factors is analyzed in detail. Estimation error results in larger amplitude of the stator current, the larger loss, and the degradation of the output torque. To eliminate the estimation error, a novel correction method based on the “minimu-current-tracking (MCT)” method is proposed in this article. The main idea of the proposed method is to obtain the minimum amplitude of the stator current adaptively through the MCT algorithm. The estimation error can be reduced to zero by correcting the compensation factor of the designed position estimator and observing the amplitude of stator current until the amplitude converges to the minimum value. The proposed method has two advantages: 1) the estimation error caused by no matter which kind of nonideal factor can be completely corrected; 2) no sensitive parameters are required in the proposed method. Finally, sufficient simulated results and experimental results verify the effectiveness of the proposed MCT-based method.

Design and Experimental Validation of an Adaptive Sliding Mode Observer-Based Fault-Tolerant Control for Underwater Vehicles

Cost and other practically related reasons can mean that velocity sensors are not available on an underwater vehicle. For such cases, the results in this brief are developed on an observer-based fault-tolerant control for underwater vehicles in the presence of external disturbances and unknown thruster faults. An adaptive sliding mode observer is developed to achieve finite-time convergence where, in comparison to a high-gain-based design for the observer, a nonlinear feedback is constructed based on the position estimation error. Unlike alternatives, a discontinuity term in the developed fault tolerant controller is avoided, and the stability of the controlled dynamics is characterized using the Lyapunov theory. Finally, these new results are supported by both a simulation-based study and experimental verification.

Angle Tracking Observer with Improved Accuracy for Resolver-to-Digital Conversion

A resolver is an absolute shaft sensor which outputs pair signals with ortho-symmetric amplitudes. Ideally, they are sinusoidal and cosinusoidal functions of the shaft angle. In order to demodulate angular position and velocity from resolver signals, resolver-to-digital conversion (RDC) is necessary. In software-based RDC, most algorithms mainly employ a phase-locked loop (PLL)-based angle tracking observer (ATO) to form a type-II system. PLL can track the detected angle by regulating the phase error from the phase detector which depends on the feature of orthogonal symmetry in the resolver outputs. However, a type-II system will result in either steady-state errors or cumulative errors in the estimation of angular position with constant accelerations. Although type-III ATOs can suppress these errors, they are still vulnerable to high-order acceleration signals. In this paper, an improved PLL-based ATO with a compensation model is proposed. By using dynamic compensation, the proposed ATO becomes a type-IV system and can reduce position estimation errors for high-order acceleration signals. In addition, the parameters of ATO can be tuned according to the bandwidth, noise level and capability of error suppression. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Position Sensorless Control for Doubly Salient Electromagnetic Machine With Improved Startup Performance

It is an effective approach to estimate rotor position of doubly salient electromagnetic machine (DSEM) from self-inductances, which changes with rotor position. To overcome the restrictions of low estimation accuracy as well as the inevitable commutation lag in the conventional sensorless startup method, this paper proposes a novel rotor angular position estimating technique for the DSEM sensorless startup. It starts with injecting three test pulses A + C- (phase A is connected to the positive rail of dc bus, and phase C is connected to the ground rail), B + A- and C + B- on armature windings, so two-phase series self-inductances can be identified according to the resultant currents. Then, the estimating expression of the DSEM rotor angular position is further obtained based on the geometrical relation in the rectangle composed of self-inductances, and the inevitable commutation lag in startup process is likely to be eliminated with the estimated rotor angular position. Moreover, the error of rotor angular position introduced by the nonideal linearity of self-inductances are analyzed in detail, and the boundaries of neighboring sectors are distinguished from normal sectors to compensate the estimation error of rotor angular position. Finally, to enhance the output torque performance during the DSEM startup process, a novel selection method for test pulses concerning rotor sector is developed. Compared to the conventional method, a quicker and superior startup performance is achieved since no negative torque is generated by the test pulses. The experiments on a 12/8-pole DSEM validate the correctness and feasibility of the proposed sensorless control methods.

A resilient adaptive sliding mode observer for sensorless AC salient pole machine drives based on an improved HF injection method

In high frequency (HF) injection methods, classical tracking algorithms are used to estimate the rotor position for sensorless alternating current (AC) salient pole machines. These algorithms were dependent on AC machine inductances which are characterized by their large variations. To overcome this dependency, a new approach based on using only the sign of the rotor position estimation error (instead of using rotor position estimation error) is proposed. This approach has also the advantage of removing the low-pass filter (LPF) used to separate the HF component from the rotor position information. Consequently, only the first order sliding mode observer can be employed to estimate the rotor position, as only the sign of the rotor position estimation error is known. To avoid the well known chattering phenomena of this observer, an adaptive step-by-step sliding mode observer is proposed as an alternative solution to estimate the rotor position of the machine. The stability study of the proposed observer is analyzed both in transient/steady state ranges and a procedure for gains tuning is then given. The performance of the proposed algorithm is tested on simulation and experimentally in the framework of a representative small-scale electric propulsion benchmark, used in automotive applications. Moreover, a comparison study is conducted with respect to some existing tracking algorithms in order to illustrate the well-founded of the designed algorithm.

Efficient autofocus of small multi‐rotor UAV SAR by minimum entropy BP algorithm

Small multi-rotor unmanned aerial vehicles (SMR-UAVs) are a promising platform for low-cost synthetic aperture radar (SAR) systems. However, SMR-UAVs usually suffer from serious position errors due to their unstable motion and low actuary position sensors, and autofocus is an indispensable step for their high-quality imaging. An efficient back-projection autofocus method is proposed for SMR-UAV SAR systems by the principle of minimum entropy. The position error estimation model via minimum entropy is derived. The conjugate-gradient method is used to efficiently estimate the position errors. Moreover, to improve the computing efficiency, the strong scatterer areas are estimated as the input of entropy estimation. The effectiveness of the algorithm is demonstrated using both simulation and experimental data.

GLONASS-Aided High-Precision Navigation of Space Geodetic Systems

The paper considers the methods of high-precision navigation of space geodetic systems. A technology of determining the orbit parameters by kinematic and dynamic methods using GLONASS signals is proposed. It is for the first time that experimental estimates of position errors have been obtained in a study case of Geo-IK-2 spacecraft, with the measurement residuals of the global quantum-optical network being at the level of 0.06 m (RMS).

Sliding-Mode Sensorless Control of PMSM With Inverter Nonlinearity Compensation

In this paper, a robust adaptive sliding-mode observer (SMO) is designed based on the surface permanent-magnet synchronous machine (SPMSM) model in rotor reference frame ( $\gamma \delta $ -axis), and an online inverter nonlinearity identification and compensation method is proposed. In order to reduce the chattering of the SMO, an adaptive law is presented to help estimate the back electromotive forces of an SPMSM; thus, smaller gains can be set for switching functions of the SMO. The small-signal model of the proposed sensorless scheme is derived for analyzing the steady-state and dynamic behavior of the sensorless scheme. Voltage distortion, caused by nonlinear characteristics of switching devices, not only causes (6 k ± 1)th harmonics in phase currents but also leads to a rotor position estimation error. The equivalent amplitude of the voltage distortion can be identified based on the derived small-signal model of the proposed sensorless scheme. To improve the accuracy of the estimated voltage distortion, a recursive restricted total least squares is applied to obtain the estimated amplitude of the voltage distortion. Experimental results validate the proposed sensorless control scheme and its effectiveness.

Secrecy Rate Analysis of Open-Loop Analog Collaborative Beamforming Under Position Estimation Error of Virtual Antenna Array

As the importance to guarantee confidentiality against security attacks by eavesdroppers has attracted significant attention, there has been much effort to enhance the security performance through cooperative beamforming (CB) in the context of physical layer security (PLS). In particular, analog CB (ACB), which requires vastly lower overhead compared to digital CB, can provide significantly higher secrecy rate compared to the PLS methods with co-located array. In this letter, we focus on ACB with open-loop architecture that requires low complexity, and consider phase offsets across multiple virtual antenna array (VAA) elements for ACB, which are caused by position estimation error of VAA. We investigate the impact of the phase offsets in ACB in terms of the secrecy throughput loss.