Local Navigation Research Articles

Novel Deep Hybrid and Ensemble Algorithms for Improving GPS Navigation Positioning Accuracy

Novel Deep Hybrid and Ensemble Algorithms for Improving GPS Navigation Positioning Accuracy

A Vehicle-Carried INS Positioning Accuracy Improvement Method by Using Lateral Constraint in GPS-Denied Environment

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Analysis of the accuracy of EGNOS+SDCM positioning in aerial navigation

The article presents a modified scheme of determining the accuracy parameter of SBAS (Satellite Based Augmentation System) positioning with use of two supporting systems: EGNOS (European Geostationary Navigation Overlay Service) and SDCM (System of Differential Correction and Monitoring). The proposed scheme is based on the weighted mean model, which combines single solutions of EGNOS and SDCM positions in order to calculate the accuracy of position-ing of the aerial vehicle. The applied algorithm has been tested in a flight experiment conducted in 2020 in north-eastern Poland. The phase of approach to landing of a Diamond DA 20-C1 aircraft at the EPOD airport (European Poland Olsztyn Dajtki) was subjected to numerical analysis. The Septentrio AsterRx2i geodesic receiver was installed on board of the aircraft to collect and record GPS (Global Positioning System) observations to calculate the naviga-tion position of the aircraft. In addition, the EGNOS and SDCM corrections in the “*.ems” format were downloaded from the real time server data. The computations were realized in RTKPOST library of the RTKLIB v.2.4.3 software and also in Scilab application. Based on the conducted research, it was found that the accuracy of aircraft positioning from the EGNOS+SDCM solution ranged from -1.63 m to +3.35 m for the ellipsoidal coordinates BLh. Additionally, the accuracy of determination of the ellipsoidal height h was 1÷28% higher in the weighted mean model than in the arith-metic mean model. On the other hand, the accuracy of determination of the ellipsoidal height h was 1÷28% higher in the weighted mean model than for the single EGNOS solution. Additionally, the weighted mean model reduced the resultant error of the position RMS-3D by 1÷13% in comparison to the arithmetic mean model. The mathematical model used in this study proved to be effective in the analysis of the accuracy of SBAS positioning in aerial navigation

A Study on the Usage of Robot Navigation to Determine Robot’s Own Position in its Frame of Reference and then to Plan a Path towards Some Goal Location

In this study, the issues and opportunities can be achieved with the help of a robotic navigation system. The determination of a robotic navigational system is able to ensure the proper development of different understandings which are achieved with the help of different problems as these can solve different issues. Navigational robots are used in different fields and provide great growth in these fields which are effective and promotional developments are understood. Drones are used for different navigational improvements such as mapping which provide accuracy in the system as these are beneficial for gaining growth in goal achievement through different AI-based software systems. Secondary qualitative research is done for the completion of the research which is able to provide proper knowledge in this research in the field of robot navigation. Nowadays navigation can be easily accessible with the help of GPS systems as people are able to get the location of any place from anywhere on the internet. These facilities are ensuring proper development in the business process of companies as different businesses achieve their profitability with the help of a robotic navigational system that has gained progressive development in the research. The understanding of the robot's own location is able to develop the quality of the robot's ability which are helping to ensure planning toward the goal location. Real-time navigation is creating different ways for aiming towards navigational path planning which are helping to achieve material handling, patrolling, disaster relief, and rescue operations. Keywords : Artificial potential field, autonomous navigation, Dijkstra, extended Kalman filter, Global Navigation Satellite System, Global Positioning System, hybridization techniques, LIDAR, local navigation system, near fields communication, Robotic navigation, Vector field histograms.

Analysis on navigation accuracy of high orbit spacecraft based on BDS GEO and IGSO satellites

To make up for the insufficiency of earth-based TT&C systems, the use of GNSS technology for high-orbit spacecraft navigation and orbit determination has become a new technology. It is of great value to applying Geosynchronous Earth Orbit (GEO) and Inclined GeoStationary Orbit (IGSO) navigation satellites for supporting the navigation of high-orbit spacecraft since there are three different types of navigation satellites in BeiDou Navigation Satellite System (BDS): Medium Earth Orbit (MEO), GEO and IGSO. This paper conducts simulation experiments based on Two-Line Orbital Element (TLE) data to analyze and demonstrate the role of these satellites in the navigation of high-orbit spacecraft. Firstly, the spacecraft in GEO was used as the target satellite to conduct navigation experiments. Experiments show that for the spacecraft on the GEO orbit, after adding GEO and IGSO respectively on the basis of receiving MEO navigation satellite signals, the accuracies were improved by 7.22 % and 6.06 % respectively. When adding both GEO and IGSO navigation satellites at the same time, the accuracy can reach 16 m. In the second place, navigation and positioning experiments were carried out on three high elliptical orbit (HEO) satellites with different semimajor axis (32037.2 km, 42385.9 km, 67509.6 km). The experiments show that the number of visible satellites has been improved significantly after adding GEO and IGSO navigation satellites at the same time. The visible satellites in these three orbits were improved by 32.84 %, 41.12 % and 37.68 %, respectively compared with only observing MEO satellites.The RMS values of the navigation positioning errors of these three orbits are 25.59 m, 87.58 m and 712.48 m, respectively.

Visual Mapping and Localization System Based on Compact Instance-Level Road Markings With Spatial Uncertainty

High-definition (HD) map is crucial for intelligent vehicles to perform high-level localization and navigation. To improve the availability and usability of HD map, it is meaningful to investigate crowd-sourced mapping solutions and low-cost map-aided localization schemes which don't rely on high-end sensors. In this letter, we propose a novel vision-based mapping and localization system, which could generate compact instance-level road maps automatically and provide high-availability map-aided localization. The spatial uncertainties of the map elements are taken into consideration by analyzing the inverse perspective mapping (IPM) model, which enables more flexible map usages in both mapping and localization phases of the system. Besides, a pose graph optimization framework is developed for accurate pose estimation by fusing global positioning (GNSS), local navigation (odometry) and map matching information together. Real-world experiments in urban environment were conducted to validate different phases of the system, including on-vehicle mapping, multi-source map merging and map-aided localization.

S311 Following Through on Positive Fecal Immunochemical Testing via FIT Navigation and a Direct-to-Colonoscopy Pathway: Preparing for the Next Wave

S311 Following Through on Positive Fecal Immunochemical Testing via FIT Navigation and a Direct-to-Colonoscopy Pathway: Preparing for the Next Wave

Study of the LQRY-SMC Control Method for the Longitudinal Motion of Fully Submerged Hydrofoil Crafts

The control system is one of the important components of the hydrofoil craft. By adjusting the navigation attitude of the craft, the hydrofoil craft can navigate stably and safely in the turbulent environment. Aiming at the problem that existing control algorithms have poor stability in the longitudinal motion control of hydrofoil craft, the longitudinal motion reduction is limited, and there are excessive requirements for accurate disturbance wave data. Based on the fully submerged hydrofoil craft model, this article proposes a joint control method LQRY-SMC combining linear-quadratic optimal control with output regulation (LQRY) and sliding-mode control (SMC), and adds genetic algorithm to optimize the weighting matrix parameters, get better control-feedback gain, improve the global optimal-control stability, thus improving the comfort of the crew, and prevent the attack of the hull, deck wetness and damage to instruments. The simulation results show that compared with the existing methods, the heave displacement and pitch angle obtained by LQRY-SMC under the turbulent flow of different significant wave heights are reduced by about 50%, and the influence of longitudinal motion on hydrofoil crafts is avoided to a large extent, which proves the effectiveness and superiority of the method proposed.

A photoacoustics-enhanced drilling probe for radiation-free pedicle screw implantation in spinal surgery.

This article proposes a novel intra-operative navigation and sensing system that optimizes the functional accuracy of spinal pedicle screw implantation. It does so by incorporating radiation-free and multi-scale macroscopic 3D ultrasound (US) imaging and local tissue-awareness from in situ photoacoustic (PA) sensing at a clinically relevant mesoscopic scale. More specifically, 3D US imaging is employed for online status updates of spinal segment posture to determine the appropriate entry point and coarse drilling path once non-negligible or relative patient motion occurs between inter-vertebral segments in the intra-operative phase. Furthermore, a sophisticated sensor-enhanced drilling probe has been developed to facilitate fine-grained local navigation that integrates a PA endoscopic imaging component for in situ tissue sensing. The PA signals from a sideways direction to differentiate cancellous bone from harder cortical bone, or to indicate weakened osteoporotic bone within the vertebrae. In so doing it prevents cortical breaches, strengthens implant stability, and mitigates iatrogenic injuries of the neighboring artery and nerves. To optimize this PA-enhanced endoscopic probe design, the light absorption spectrum of cortical bone and cancellous bone are measured in vitro, and the associated PA signals are characterized. Ultimately, a pilot study is performed on an ex vivo bovine spine to validate our developed multi-scale navigation and sensing system. The experimental results demonstrate the clinical feasibility, and hence the great potential, for functionally accurate screw implantation in complex spinal stabilization interventions.

Multi-Objective Instantaneous Center of Rotation Optimization Using Sensors Feedback for Navigation in Self-Reconfigurable Pavement Sweeping Robot

Pavement in outdoor settings is an unstructured environment with sharp corners, varying widths, and pedestrian activity that poses navigation challenges while cleaning for autonomous systems. In this work, an approach towards navigating without collision in constrained pavement spaces using the optimal instantaneous center of rotation (ICR) is demonstrated using an in-house developed omnidirectional reconfigurable robot named Panthera. The Panthera reconfigurable design results in varying footprints, supported by passive linear joints along the robot width, with locomotion and steering action using four wheels independent steering drive (4WISD). The robot kinematics and perception sensors system are discussed. Further, the ICR selection is carried out using multi-objective optimization, considering functions for steering, varying width, distance, and clearance to avoid a collision. The framework is incorporated in a local navigation planner and demonstrated experimentally in real pavement settings. The results with optimal selection of ICR in two dimensional space within the robot footprint successfully perform smooth navigation in the constraint space. It is experimentally highlighted with four different scenarios, i.e., constraint conditions encountered by a robot during navigation. Moreover, the formulation of optimal selection of ICR while avoiding collision is generic and can be used for other mobile robot architectures.

How Rough Is the Path? Terrain Traversability Estimation for Local and Global Path Planning

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method.

Analysis of the Influence of Attitude Error on Underwater Positioning and Its High-Precision Realization Algorithm

GNSS/INS can provide position, attitude, and velocity (PAV) information for shipborne platforms. However, if the ship has a long-term linear motion or a stationary state, and is under the combined actions of sea surface swells, there will be a situation of sideslip and drift; if the ship is traveling slowly or shaking violently, the attitude calculation will not be completed. In the above situation, the traditional single-antenna GNSS/INS measurement mode is not suitable, and the attitude observability is poor; the heading angle attitude information, especially, will gradually diverge. Unreliable information will directly lead to a significant increase in underwater positioning errors. In this paper, a multi-antenna GNSS/INS combination algorithm is developed and used to provide high-precision PAV information, and is thereby able to obtain high-precision underwater positioning results. The experimental results show that the method has improved the acquisition of position and velocity in the horizontal direction and the accuracy of navigation attitude measurement. In particular, the attitude measurement accuracy in the 3 degrees of freedom (DoF) are improved by 10.1% (roll), 8.6% (pitch), and 29.3% (yaw).

Development and experimental analysis of a small integrated edge navigation sensor based on principle of circular arc array.

Signal, accuracy, and real-time performance of satellite, radar, and machine vision is a subject of concern in various complex agricultural environments. Therefore, the demand for a robust navigation sensor for indoor and vertical agricultural environment remains crucial, and it is a significant subject. In view of this, the relative edge pose detection method based on the ideal target band principle of the lateral center arc array, in this research, a small integrated arc array navigation sensor module based on adaptive detection arc technology, is developed, which costs about $100, autonomous edge navigation position, and attitude detection is realized in facility agriculture environment with continuous structured corridor or roadside features. In this research, a coupling method of reducing the radius of distance sensor arrangement, adjusting the unequal center angle, and increasing the detection distance is used to realize the miniaturization of the arc array arrangement. A semicircular modular rocket was designed to slide and adjust the center angle of the distance sensor, and the longitudinal installation position of the modularized sensor was adjusted by translating the circular arc of the detection; the convenient moving arrangement under different vehicle width and detection arc characteristics is realized. An adaptive construction method of detecting a circular arc based on self-calibrating detection distance of a distance sensor is proposed, which effectively reduces the difficulty of arranging the lateral central circular arc array; the fast construction of lateral detection arc is realized. In addition, in order to improve the accuracy and stability of the pose detection, the Mahalanobis distance algorithm and the standard Kalman filter are used to optimize the estimation of the ranging information and the relative pose of the edge. The experimental results show that the small integrated arc array navigation sensor module can independently construct photoelectric detection arcs with different characteristics to detect the position and attitude of the relative edge. When the road surface is concave and convex, the small integrated arc array navigation sensor module can still maintain the stable position and attitude detection of the relative edge for more than 30 s. In addition, when the walking speed of the autonomous navigation platform is 0.15 m/s to 0.35 m/s, the detection errors of lateral deviation and heading deviation relative to the road edge are less than 40 mm and 4.5°, respectively. The small integrated arc array navigation sensor module is less affected by the change of operating speed, and still has good accuracy and stability. The results show that the modularized edge navigation sensor has the advantages of fast and convenient use, high accuracy, and low cost; it can be applied to autonomous edge navigation control in greenhouse and plant and animal factories.

Association of Funding Cuts to the Patient Protection and Affordable Care Act Navigator Program With Privately Sponsored Television Advertising

Many individuals eligible for coverage in the Patient Protection and Affordable Care Act (ACA) marketplace remain unenrolled because of information barriers. Whether the private sector or the public sector should conduct outreach to address these barriers is a topic of active debate. To determine whether cuts to the funding of the ACA navigator program were associated with changes in the volume of private sector advertising. Using data from the 2015 to 2019 open enrollment periods, this economic evaluation analyzed the changes in advertising associated with 2017 to 2019 cuts to navigator program funding. A difference-in-difference analysis was used to compare outcomes before and after the cuts in counties with higher and lower exposure to the navigator program. Health insurance advertising was measured using data from Kantar/Campaign Media Analysis Group in collaboration with the Wesleyan Media Project, the most comprehensive data available on local broadcast and national cable advertising. The data set included all counties that met the eligibility criteria for the navigator program from 2015 through 2019. Data were analyzed from August 2021 to May 2022. Counties were classified as having higher or lower exposure to the navigator program according to the intensity of program activity in 2016, before the funding cuts. Counties served only by statewide navigator programs were categorized as lower exposure, while those also served by local navigator programs were categorized as higher exposure. Number of privately sponsored television advertisement airings for the ACA individual health insurance marketplace during the 2015 to 2019 open enrollment periods in each county, adjusted for population. All counties in 33 states that met the eligibility criteria for the navigator program from 2015 through 2019 were included in the analysis (2435 counties). Cuts to the navigator program were not associated with changes in the number of privately sponsored health insurance advertisements aired. Results were similar under several alternative approaches including an event study specification. In this study of the association between television advertising and navigator funding in the ACA marketplaces, private sector entities did not increase their advertising to compensate for declines in government-sponsored navigator activity. This finding can inform policy debates about the extent to which the private sector adjusts in response to changes in government outreach, and thus improve the design of state waivers and federal funding allocations.

ALAN-P: Dynamic action pruning for efficient navigation in complex environments

Multi-agent navigation consists on efficiently moving a set of agents from start to goal locations. This is a challenging task since most environments contain static and dynamic obstacles that can significantly restrict the movement of an agent. While existing methods, such as ALAN, can overcome some of these limitations by increasing the action space of the agents, their behavior can be suboptimal in many of the situations that agents can find themselves into. In this work, we propose ALAN-P, a multi agent local navigation method based on the ALAN framework, which improves the agent's behavior by dynamically adapting the action space to the agent's local conditions. The results of our experiments show that the proposed ALAN-P can lead to significant performance improvements over ALAN in a variety of challenging environments.

ACCURACY ANALYSIS OF AIRCRAFT POSITIONING USING GPS DUAL RECEIVERS IN AERIAL NAVIGATION

This study presents a modified algorithm to determine the accuracy of GPS positioning in aerial navigation. To achieve this, a mixed model with measurement weights was used to determine the resultant value of accuracy of aerial vehicle positioning. The measurement weights were calculated as a function of the number of GPS tracking satellites. The calculations were performed on actual GPS measurement data recorded by two onboard GNSS receivers installed onboard a Cessna 172 aircraft. The flight test was conducted around the military airport in Dęblin. The conducted analyses demonstrated that the developed algorithm improved the accuracy of GPS positioning from 62 to 91% for horizontal coordinates and between 16-83% for the vertical component of the aerial vehicle position in the BLh ellipsoidal frame. The obtained test results show that the developed method improves the accuracy of aircraft position and could be applied in aerial navigation.

Method for determining of statistical characteristics of evaluated distance obtained from measurements of Doppler frequency offsets

This paper presents a technique for obtaining of the main statistical characteristics of the distance estimation between the flying object and a hyperbolic navigation system using two Doppler frequency shifts of the navigation signal. This problem arises when the guidance of the object at a target point along a hyperbola using a bistatic hyperbolic navigation system is considered. Despite the fact that the object guidance loop is closed according to the time difference of arrival information, it is necessary to enter the distance between the object and the center of the navigation system into the algorithm for generating control commands in order to stabilize the frequency response of the control loop and eliminate dynamic errors. This article discusses the statistical characteristics of the estimated range based on the measurements of three values of the object’s speed: its own speed and two projections of this speed on the lines connecting the object with the navigation positions.

APPLICATION OF SATELLITE STATION DIFFERENTIAL TECHNOLOGY IN REMOTE AREAS

Abstract. Aiming at the situation that there is no network and communication signal in remote areas, the idea of using satellite station difference technology to obtain relatively accurate positioning is proposed. This paper briefly introduces the current mainstream satellite station differential system, and carries out static and dynamic experiments using satellite station differential technology in the remote Ali Region of Western China. The experimental results show that the selection of satellite station differential system, the selection of receiver, the surrounding environment during observation and the motion state during observation all affect the positioning accuracy. Under the static observation conditions, the satellite station differential can obtain an accuracy better than 0.15m, and in the dynamic At low speed, the precision of 2m can be obtained. The experimental results show that the satellite station differential positioning technology is generally reliable and can be applied to many applications, such as control point measurement, position navigation and so on.

A Trident Quaternion Framework for Inertial-Based Navigation—Part II: Error Models and Application to Initial Alignment

This work deals with error models for trident quaternion framework proposed in the companion paper (Part I) and further uses them to investigate the odometer-aided static/in-motion inertial navigation attitude alignment for land vehicles. By linearizing the trident quaternion kinematic equation, the left and right trident quaternion error models are obtained, which are found to be equivalent to those derived from profound group affine. The two error models are used to design their corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF). Simulations and field tests are conducted to evaluate their actual performances. Owing to the high estimation consistency, the L/RQEKF converge much faster in the static alignment than the traditional error model-based EKF, even under arbitrary large heading initialization. For the in-motion alignment, the L/RQEKF possess much larger convergence region than the traditional EKF does, although they still require the aid of attitude initialization so as to avoid large initial attitude errors.

Adaptive trajectory estimation with power limited steering model under perturbation compensation

Trajectory estimation of maneuvering objects is applied in numerous tasks like navigation, path planning and visual tracking. Many previous works get impressive results in the strictly controlled condition with accurate prior statistics and dedicated dynamic model for certain object. But in challenging conditions without dedicated dynamic model and precise prior statistics, the performance of these methods significantly declines. To solve the problem, a stochastic nonlinear model called the power-limited steering model is proposed to describe the motion of non-cooperative object. It is a natural combination of instantaneous power and instantaneous angular velocity, relying on the nonlinearity to achieve the change of states. And the renormalization group is introduced to compensate the nonlinear effect of perturbation in our model. For robust and efficient trajectory estimation, an adaptive trajectory estimation (AdaTE) algorithm is proposed. By updating the statistics and truncation time online, it corrects the estimation error caused by biased prior statistics and observation drift, while reducing the computational complexity lower than O(n). The experiment of trajectory estimation demonstrates the convergence of AdaTE, and the better robust to the biased prior statistics and the observation drift compared with several typical estimation algorithms. Other experiments demonstrate through slight modification, our method can also be applied to local navigation in random obstacle environment, and trajectory optimization in visual tracking.