Navigation Errors Research Articles

Downrange Estimation Based on Powered Explicit Guidance for Pinpoint Lunar Landing

Onboard computation of a fuel-optimal steering law is a pivotal technology for future lunar and planetary campaigns with pinpoint landings. This paper proposes a method of downrange estimation based on improved powered explicit guidance (PEG) focusing on the existing throttleable engine. The main contribution includes two aspects. First, the conventional PEG algorithm is improved to enforce prediction accuracy. The prediction of lander cutoff state can be used to estimate downrange position, and thus, the thrust switching time is corrected to recalculate optimal steering law. Second, in consideration of the thrust identification error and navigation uncertainty, the thrust switching condition triggered by the state of the lander instead of time is established. The thrust is switched depending on estimated downrange feedback, and thus, a pinpoint landing with near-optimal fuel is achieved. The effectiveness of the proposed algorithm are demonstrated through Monte Carlo simulations in the presence of thrust deviation and navigation error.

Design and Implementation of Intelligent Security Robot Based on Lidar and Vision Fusion*

Nowadays, the mainstream security robot is expensive and the single 2D Lidar SLAM robot has large pose error and large matching calculation in navigation. To solve these problems, this paper designs an intelligent security robot based on the ROS system and STM32 microcontroller. Through the fusion of camera, IMU, lidar multi-sensor data, completing its own positioning, surrounding environment mapping and autonomous navigation functions; Binocular camera for face recognition, lock suspicious persons; Combined with temperature and humidity, smoke and other sensors for environmental monitoring, real-time alarm; Security personnel remote control, according to the data uploaded by the robot to complete the patrol. After a lot of experiments, the functions of the robot are better completed, which has a certain practical value.

Local positioning system as a classic alternative to atomic navigation

A local positional system (LPS) is proposed, in which particles are launched at given velocities, and a sensor system measures the trajectory of the particles in the platform frame. These measurements allow us to restore the position and orientation of the platform in the frame of the rotating Earth, without solving navigation equations. When the platform velocity is known and if the platform orientation stays the same, the LPS technique allows a navigational accuracy of 100 $\mu$m per one hour to be achieved. In this case, the LPS technique is insensitive to the type of platform trajectory. If there are also velocimeters installed on the platform, then one can restore the platform velocity and angular rate of the platform rotation with respect to the Earth. Instead of navigational equations, it is necessary to obtain the classical trajectory of a particle in the field of a rotating gravity source. Taking into account the gravity-gradient, Coriolis, and centrifugal forces, the exact expression for this trajectory is derived, which can be widely used in atomic interferometry. A new iterative method for restoring the orientation of the platform without using gyroscopes is developed. The simulation allows us to determine the conditions under which the LPS navigation error per hour is approximately $10$ m.

The covariance matrix transformation method in all-earth integrated navigation considering coordinate frame conversion

In the exploration of polar regions, navigation is one of the most important issues to be resolved. To avoid the limitations of a single navigation coordinate frame, navigation systems usually use different navigation coordinate frames in polar and nonpolar regions, such as the north-oriented geographic frame and the grid frame. However, the error states and covariance matrix are related to the definition of the navigation coordinate frame, since coordinate frame conversion will cause overshoot of the integrated navigation Kalman filter and error discontinuity. To solve this problem, the transformation relationship of error states defined in different frames is deduced, whereby the covariance matrix transformation relationship is also analyzed. On this basis, covariance transformation-based open-loop and closed-loop Kalman filter integrated navigation algorithms are proposed. The effectiveness of the algorithms is verified by flight tests with a rotational strapdown inertial navigation system/global navigation satellite system integrated navigation system.

Calibration method for misalignment angles of a fiber optic gyroscope in single-axis rotational inertial navigation systems.

In a fiber optic gyroscope rotational inertial navigation system (RINS), attitude errors may change after vibration due to the change of misalignment angles. There are two kinds of misalignment angles which can cause the same attitude errors: the one is misalignment angles of gyroscopes, and the other is misalignment angles between input axis of gyroscope and rotating gimbal axis. Thus, it is difficult to calibrate any kind of misalignment angles by attitude errors alone. Self-calibration methods can separate and calibrate the two kinds of misalignment angles. But single-axis RINSs rely on a turntable to realize the rotation scheme. And misalignment angles may change during repeated removal. Therefore, it is necessary to study an efficient and convenient method to analyze which kind of misalignment angles leads to the change of attitude errors and calibrate these misalignment angles. According to the different influences of two kinds of misalignment angles on navigation errors and fine alignment errors, this paper proposes a calibration method based on fine alignment algorithm to calibrate the gyroscopes' misalignment angles. Its accuracy is proven by simulations and experiments. From experimental results, position errors have decreased at least 21.4% with the proposed method.

Computer Vision in Self-Steering Tractors

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

AbstractTo achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

A Simplified Worldwide Ionospheric Model for Satellite Navigation

Ionospheric delay is one of the main sources of error in satellite positioning and navigation. Ionospheric broadcast parameters are required to correct for the ionospheric delay, especially in the case of massive single-frequency users. The well-known Klobuchar model has been providing the ionospheric delay corrections for GPS users during the last several decades. For Galileo users, a three-dimensional electron density model (NeQuick G) based on the NeQuick climatological model is used to correct for ionospheric delay. The recently completed Chinese BeiDou Satellite Navigation System (BDS) broadcasts two types of ionospheric models coefficients on different signals. In this article, we propose a simplified worldwide ionospheric model (SWIM) for satellite positioning. By comparison, our SWIM model has better performance than the Klobuchar model in terms of accuracy at both low and high solar activity, and is comparable to the NeQuick G and BDS ionospheric models. At the same time, the SWIM model has high efficiency because of the simple calculation process. It has great potential for ionospheric delay corrections in satellite navigation and positioning.

Head-Mounted Augmented Reality Platform for Markerless Orthopaedic Navigation.

Visual augmented reality (AR) has the potential to improve the accuracy, efficiency and reproducibility of computer-assisted orthopaedic surgery (CAOS). AR Head-mounted displays (HMDs) further allow non-eye-shift target observation and egocentric view. Recently, a markerless tracking and registration (MTR) algorithm was proposed to avoid the artificial markers that are conventionally pinned into the target anatomy for tracking, as their use prolongs surgical workflow, introduces human-induced errors, and necessitates additional surgical invasion in patients. However, such an MTR-based method has neither been explored for surgical applications nor integrated into current AR HMDs, making the ergonomic HMD-based markerless AR CAOS navigation hard to achieve. To these aims, we present a versatile, device-agnostic and accurate HMD-based AR platform. Our software platform, supporting both video see-through (VST) and optical see-through (OST) modes, integrates two proposed fast calibration procedures using a specially designed calibration tool. According to the camera-based evaluation, our AR platform achieves a display error of 6.31 ± 2.55 arcmin for VST and 7.72 ± 3.73 arcmin for OST. A proof-of-concept markerless surgical navigation system to assist in femoral bone drilling was then developed based on the platform and Microsoft HoloLens 1. According to the user study, both VST and OST markerless navigation systems are reliable, with the OST system providing the best usability. The measured navigation error is 4.90 ± 1.04 mm, 5.96 ± 2.22 ° for the VST system, and 4.36 ± 0.80 mm, 5.65 ± 1.42 ° for the OST system.

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

In response to the traditional WiFi location fingerprint positioning algorithm still having a low positioning accuracy, which is difficult to meet the robot indoor positioning and navigation needs, a series of improvements are made to the traditional WiFi location fingerprint positioning algorithm, so that the positioning accuracy of the algorithm can be effectively improved. At the stage of building the location fingerprint library offline, WiFi signals are collected at each reference point by reducing the reference point spacing of the traditional location fingerprinting algorithm and then using different time period collection methods. The WiFi signal strength values are standardized using the standardization processing method to improve the specificity of traditional location fingerprinting. In the real-time localization stage, the WiFi signals collected from the unknown location points are averaged, and then, the fingerprint similarity calculation is performed using a matching method based on the magnitude of the Marxian distance as a similarity reference. In order to eliminate the location fingerprints that degrade the localization accuracy, an improved adaptive K -value WKNN algorithm is integrated at the end of the localization algorithm. The improved localization algorithm and the proposed raster-based navigation algorithm are validated in a fixed experimental environment. The experimental results show that the probability of the improved algorithm’s positioning error within 0.4 m is 49%, which is a 35% improvement over the conventional algorithm. Combining the improved positioning algorithm with our proposed grid-based navigation algorithm, the final navigation error probability within 0.8 m is 62%.

Position Correction and Trajectory Optimization of Underwater Long-Distance Navigation Inspired by Sea Turtle Migration

Accumulating evidence suggests that migrating animals store navigational “maps” in their brains, decoding location information from geomagnetic information based on their perception of the magnetic field. Inspired by this phenomenon, a novel geomagnetic inversion navigation framework was proposed to address the error constraint of a long-distance inertial navigation system. In the first part of the framework, the current paper proposed a geomagnetic bi-coordinate inversion localization approach which enables an autonomous underwater vehicle (AUV) to estimate its current position from geomagnetic information like migrating animals. This paper suggests that the combination of geomagnetic total intensity (F) and geomagnetic inclination (I) can determine a unique geographical location, and that there is a non-unique mapping relationship between the geomagnetic parameters and the geographical coordination (longitude and latitude). Then the cumulative error of the inertial navigation system is corrected, according to the roughly estimated position information. In the second part of the framework, a cantilever beam model is proposed to realize the optimal correction of the INS historical trajectory. Finally, the correctness of the geomagnetic bi-coordinate inversion localization model we proposed was verified by outdoor physical experiments. In addition, we also completed a geomagnetic/inertial navigation integrated long-distance semi-physical test based on the real navigation information of the AUV. The results show that the geomagnetic inversion navigation framework proposed in this paper can constrain long-distance inertial navigation errors and improve the navigation accuracy by 73.28% compared with the pure inertial navigation mode. This implies that the geomagnetic inversion localization will play a key role in long-distance AUV navigation correction.

Radiometric Stability Assessment of the DSCOVR EPIC Visible Bands Using MODIS, VIIRS, and Invariant Targets as Independent References

The DSCOVR mission was designed to take advantage of the first Lagrangian position (L1) to continuously observe the Earth sunlit disk. To facilitate EPIC V03 data product validation and fusion, the EPIC V03 navigation and calibration stability is assessed. The Aqua-MODIS, NPP-VIIRS, and N20-VIIRS based radiometric scaling factors are also provided. The V03 navigation error was 15.5 km, a 50% improvement over V02 and within what can be achieved by an objective image alignment algorithm. Both the navigation accuracy and precision were improved in V03 and were found to be comparable across all EPIC visible channels. The all-sky tropical ocean and deep convective cloud ray-matched MODIS- and VIIRS-referenced EPIC inter-calibration gains are within 0.4% of one-another, and are also within 0.4% of a previous study’s NPP-VIIRS-referenced gains. The inter-calibration study reveals that EPIC bands 5 and 6 degraded mostly within the first year of operation and becoming stable thereafter, whereas bands 7 and 10 were stable during the 6-years record. The capability of the V03 navigation allowed EPIC stability to be monitored using DCC and Libya-4 invariant targets. The EPIC V03 calibration was mostly stable within 0.3% over the 6-years record, as determined from inter-calibration and invariant target monitoring methods. Remarkably, both the DCC- and Libya-4-based methods were able to confirm the stability of the E-8 and E-9 oxygen absorptions—a stability comparable to that of the E-7 and E-10 reference bands. No significant change in the navigation accuracy or calibration stability was observed after the DSCOVR 2019 safe mode incident. The impressive stability of the DSCOVR EPIC L1B V03 channel radiances can greatly benefit the Earth remote sensing community by providing diurnally complete daytime radiative flux and environmental retrievals for future sensors located at L1.

Investigation of Using Sky Openness Ratio as Predictor for Navigation Performance in Urban-like Environment to Support PBN in UTM.

One of the causes of positioning inaccuracies in the Unmanned Aircraft System (UAS) is navigation error. In urban environment operations, multipaths could be the dominant contributor to navigation errors. This paper presents a study on how the operation environment affects the lateral (horizontal) navigation performance when a self-built UAS is going near different types of urban obstructions in real flight tests. Selected test sites are representative of urban environments, including open carparks, flight paths obstructed by buildings along one or both sides, changing sky access when flying towards corners formed by two buildings or dead ends, and buildings with reflective glass-clad surfaces. The data was analysed to obtain the horizontal position error between Global Positioning System (GPS) position and ground truth derived from Real Time Kinematics (RTK), with considerations for (1) horizontal position uncertainty estimate (EPH) reported by the GPS receiver, (2) no. of visible satellites, and (3) percentage of sky visible (or sky openness ratio, SOR) at various altitudes along the flight paths inside the aforementioned urban environments. The investigation showed that there is no direct correlation between the measured horizontal position error and the reported EPH; thus, the EPH could not be used for the purpose of monitoring navigation performance. The investigation further concluded that there is no universal correlation between the sky openness ratio (SOR) seen by the UAS and the resulting horizontal position error, and a more complex model would need to be considered to translate 3D urban models to expected horizontal navigation uncertainty for the UAS Traffic Management (UTM) airspace.

Drift-Free Visual SLAM for Mobile Robot Localization by Integrating UWB Technology

Visual simultaneous localization and mapping (vSLAM or visual SLAM) is an important technique for mobile robot localization in the global positioning system denied (GPS-denied) environments. However, the positioning accuracy could become unbearable due to the lack of image feature points when the robot navigates in a spacious indoor space. The drifting errors accumulated over time are generally inevitable and need to be mitigated by more sophisticated loop-closure algorithms. In this paper, we propose a drift-free visual SLAM technique for mobile robot localization by integrating the ultra-wideband (UWB) positioning technology. The basic concept is to utilize the global constraint of the UWB positioning to reduce the locally accumulated errors of visual SLAM localization based on the extended Kalman filtering (EKF) framework. In our experimental results, various SLAM approaches are performed in the indoor scenes, and the evaluation and comparison have demonstrated the feasibility of the proposed localization technique. By the integration of UWB positioning, the overall drift error of the robot navigation is reduced for more than 50%.

A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-Mounted Inertial Navigation System

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities (10s of g and 1000s of deg/sec) during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer’s signals with a triangular function and estimates the size of the triangles using a Gaussian Process (GP) regression. To evaluate performance of pedestrian navigation systems enhanced by the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU, which have accelerometer’s FSR of ±16 g and ±40 g, respectively. In the first series of experiments, forces experienced by the foot-mounted IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer’s readings. The saturated accelerometer’s readings reduced the accuracy of estimated positions using the VN-200 by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.34}\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3.37}\times $ </tex-math></inline-formula> along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated accelerometer’s measurements, the navigation accuracy along horizontal and vertical directions was increased by 5% and 50%, respectively, as compared to using unreconstructed signals.

Underwater Adaptive Height-Constraint Algorithm Based on SINS/LBL Tightly Coupled

The application of strap-down inertial navigation system/long baseline system (SINS/LBL) has been proven to be an effective method to solve the accumulated position error of autonomous underwater vehicles (AUVs). However, height positioning in the SINS/LBL system is generally unstable. Acoustic differential technology uses single- or double-difference mode to weaken the influence of system errors on signal propagation time, but the vertical positioning result of this method is unstable. Moreover, the accumulative errors of inertial navigation will cause the height positioning errors to diverge over time. In view of the situation that the height positioning results are prone to divergence, the SINS/LBL tightly coupled (SLT) system is studied and an adaptive method based on the improved Sage–Husa adaptive filter is proposed. This method adaptively adjusts the system noise matrix and then utilizes depth information of the pressure sensor (PS) to adaptively estimate the vertical direction errors. To verify the feasibility of the proposed algorithm, the simulation experiments compare the positioning results of three fusion methods that are centralized Kalman filter (CKF), federated Kalman filter (FKF), and adaptive height-constraint Kalman filter (AHKF). Simulation results show that the AHKF algorithm improves the precision and stability of underwater SLT navigation system significantly. To some extent, the AHKF algorithm addresses the problem of accumulative errors in the vertical direction in the SLT system. Also, the improvement of height positioning accuracy is hugely helpful to AUV complete mission successfully.

Numerical Analysis of Transcranial Magnetic Stimulation Application in Patients With Orofacial Pain.

In this paper, we monitored the accuracy of non-navigated application of repetitive Transcranial Magnetic Stimulation (rTMS) in 10 patients suffering from orofacial pain by using functional magnetic resonance (fMRI), computer modeling and numerical simulation. Through a unique process, each fMRI scan was used to define a Region of Interest (ROI) where the source of the orofacial pain was located, which was to be stimulated using rTMS. For each patient, MRI scans with a spatial resolution of 0.7 mm were converted into an anatomically accurate head model. The head model including the ROI was then co-registered with a model of the stimulation coil in an electromagnetic field numerical simulator. The accuracy of rTMS application was evaluated based on the calculations of electric field intensity distribution in the ROI. The research has yielded unique insight into ROIs (with average volume 904 mm3) in patients with orofacial pain and has also extended further possibilities of human head MRI image semi-automatic segmentation. According to the calculations performed, the average ROI volume that was stimulated by an electric field with an intensity of over 80 V/m was only 4.4%, with the maximum ROI volume being 20.5%. Furthermore, a numerical study of the impact of coil rotation and translation was performed. It demonstrated a) the optimal placement of the stimulation coil can significantly increase the volume of the stimulated ROI up to 60% and b) patients with orofacial pain would need precise coil positioning with a navigation error lower than 10 mm. Due to an acceptable proccessing time of up to 6 hours, described numerical simulation opens up new options for precise rTMS treatment planning. This planning platform together with patient-specific navigated rTMS, could lead to significant increase of treatment outcomes in patients suffering from orofacial pain.

A Dual-Axis Rotation Scheme for Long-Endurance Inertial Navigation Systems

Rotational modulation is an effective means to reduce the equivalent drifts of rotational inertial navigation system (RINS), so as to enhance the accuracy of RINS in long-endurance navigation. In the single-axis continues rotation scheme, the drift along the rotation axis cannot be modulated. In the dual-axis multi-position transposition scheme, the drifts of three gyroscopes can be modulated to zero, but, when the IMU is stationary, the inertial sensors errors lead to the rapid divergence of navigation errors. And its modulation effect on slowly varying gyro drifts may be limited. In this paper ,a modified dual-axis rotation scheme combining the above two modulation schemes is put forward. The theoretical analysis demonstrates that the three axial drifts, the scale factor errors and the installation errors can be modulated to zero. The simulation results demonstrate that the position accuracy of the proposed scheme are improved compared with the normal schemes. The effectiveness of the proposed scheme in practical application is verified by comparative experiments.

Hybrid Tracking Controller for an ASV Providing Mission Support for an AUV

Autonomous underwater vehicles (AUVs) rely on surface support for communication with operators and position fixes to bound inertial navigation errors. By installing an acoustic modem on an autonomous surface vehicle (ASV), the ASV can carry out these tasks, replacing more expensive and less flexible manned research vessels. This paper proposes a hybrid tracking controller for an ASV providing mission support for an AUV. The proposed controller keeps the ASV in a donut-shaped safety domain about the AUV defined by the risk of collision (inner boundary) and the risk of communication loss (outer boundary). At the same time, the hybrid controller reduces power consumption and acoustic signal noise by going into standby mode when it is within the safety domain. Results from a simulation study and field trials are presented to demonstrate and validate the controller's performance. The results show that the controller performed well in the tested cases.

Linear Track Underwater Carrier SINS Correction Method Based on Hydroacoustic Single Beacon

Precise positioning of underwater vehicles using only a single beacon for range measurement is one of the toughest challenges for underwater navigation, especially when the underwater vehicle travels along a straight path resulting in the system unobservable. In this paper, acoustic range measurements are combined with inertial navigation to determine the position of an underwater vehicle using two consecutive measurement points. The contributions of the work presented here are twofold: First, the underwater single beacon localization algorithm proposed in this paper is able to operate under a linear track, and give the solution of the method to reject multi-valuedness. Second, considering the actual work of underwater carriers, extending the strategy to any trajectory can be used. The results obtained by the algorithm are fed back to the inertial navigation system, which can suppress the continued dispersion of inertial navigation errors. The experimental results show that the proposed &#x201C;Linear track underwater carrier Strapdown Inertial Navigation System (SINS) correction method based on hydroacoustic single beacon&#x201D; can solve the positioning problem of underwater long-range carriers and suppress further dispersion of their inertial navigation system errors.