Precise Navigation Research Articles

Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information

As ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in the safety assessment, we present an evaluation model based on the dynamic Bayesian network (DBN) theory. Firstly, characteristic indicators are extract from marine environment systems and discretized with Cloud model. Then, the DBN is constructed through structure learning and parameter learning based on Dempster-Shafer (DS) evidence theory. Finally, the dynamic evaluation and risk zoning of the navigation safety is realized based on Bayesian probabilistic reasoning. The DBN-based assessment model fully considers the uncertainty of influence relationships between marine environment and UV navigation, and effectively fuses expert knowledge and quantitative data for assessment modeling. The experimental results show the proposed model has high reliability and good value of application.

Automated Manipulation of Microswarms Without Real-Time Image Feedback Using Magnetic Tweezers

Microswarms assembled by microparticles have shown promising prospects in targeted delivery. However, the automated manipulation of microswarms remains a considerable challenge due to the limitations of existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> imaging technology. In this article, we design a magnetic tweezer system with a large workspace of 100 mm × 100 mm × 30 mm, which can assemble, transport, and disassemble microswarms efficiently. The magnetic tweezers generate rotating magnetic fields in the workspace, enabling magnetized microparticles to roll toward a specific point along spiral trajectories. The assembly process and mechanism of microswarms are analyzed. The developed system can assemble low-density magnetic microparticles to form a stable and compact microswarm at a predetermined position. Actuation of the magnetic tweezers allows precise navigation of the swarm without relying on real-time image feedback. The experimental results show that the flexible microswarm can achieve satisfactory motion performance and transmission efficiency. The microswarm can successfully move on a slope with an inclination angle of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$40^\circ $</tex-math></inline-formula> and navigate analog channels. The overall delivery efficiency can reach 92%.

Precise orbit determination and baseline consistency assessment for Swarm constellation

Precise orbit determination (POD) and precise baseline determination (PBD) of Swarm satellites with 4 years of data are investigated. Ambiguity resolution (AR) plays a crucial role in achieving the best orbit accuracy. Swarm POD and PBD based on single difference (SD) AR and traditional double difference (DD) AR methods are explored separately. Swarm antenna phase center variation (PCV) corrections are developed to further improve the orbit determination accuracy. The code multipath of C1C, C1W and C2W observations is first evaluated and clear variations in code noise related to different receiver settings are observed. Carrier phase residuals of different time periods and different loop tracking settings of receiver are studied to explore the effect of ionospheric scintillation on POD. The reduction of residuals in the polar and geomagnetic equator regions confirms the positive impact of the updated carrier tracking loops (TLs) on POD performance. The SD AR orbits and orbits with float ambiguity (FA) are compared with the Swarm precise science orbits (PSOs). An average improvement of 27 %, 4 % and 16 % is gained in along-track, cross-track and radial directions by fixing the ambiguity to integer. For Swarm-A/B and Swarm-B/C formations, specific days are selected to perform the DD AR-based POD during which the average distance of the formation satellites is less than 5000 km. Satellite laser ranging (SLR) observations are employed to validate the performance of FA, SD AR and DD AR orbits. The consistency between the SD AR orbits and SLR data is at a level of 10 mm which shows an improvement of 25 % when comparing with the FA results. An SLR residuals reduction of 15 % is also achieved by the DD AR solution for the selected days. Precise relative navigation is also an essential aspect for spacecraft formation flying missions. The closure error method is proposed to evaluate the baseline precision in three dimensions. A baseline precision of 1–3 mm for Swarm-A/C formation and 3–5 mm for Swarm-A/B and Swarm-B/C satellite pairs is verified by both the consistency check and closure error method.

Minimally invasive and invasive liver surgery based on augmented reality training: a review of the literature.

A liver surgeon's knowledge of anatomy is critical. Due to the patient's small field of vision, patient specific, complex nerve system, and other factors, even a minor loss can result in irreversible damage. Surgeons could benefit from the use of augmented reality (AR) technology, which would bring three-dimensional image data into the operating room. AR visualization can improve surgical procedures, facilitate intraoperative planning, and enhance surgical guidance for the anatomy of interest, all of which contribute to the application's minimal invasiveness. This literature review on image guidance in liver surgery provides the reader with information about AR techniques. To ascertain the current state of Augmented reality technology's application in liver surgery, a PubMed and Embase search were conducted using the following keywords: < (Augmented reality) AND (liver surgery) > and < 'Augmented reality' AND 'liver surgery' > (publication date from January 1991 until Jun 2022). The query yielded a total of 205 publications-excluded papers in other languages, virtual reality (VR), and reviews leaving 135 studies for review. After removing duplication, the titles and abstracts of those studies were manually reviewed. Finally, 31 pertinent studies were determined to be pertinent to the subject. Generally, augmented reality technology includes preoperative planning and three-dimensional reconstruction, intraoperative three-dimensional navigation, and registration. Visualization may be aided by virtual three-dimensional reconstruction models of the liver from Computed Tomography/Magnetic Resonance Imaging scans. The results demonstrate that by utilizing augmented reality technology, blood vessels and tumor structures in the liver can be visualized during surgery, allowing for precise navigation during complicated surgical procedures. Augmented reality has been demonstrated to be safe and effective in both minimally invasive and invasive liver surgery. With recent advancements and significant effort by liver surgeons, augmented reality technologies have the potential to increase hepatobiliary surgical procedures dramatically. However, further clinical trials will be necessary to evaluate augmented reality as a tool for reducing post-operative morbidity and mortality. The impact of these cutting-edge computerized image guidance techniques on clinically relevant outcome parameters should be assessed in the future.

Star angle/ double-differenced pulse time of arrival integrated navigation method for Jupiter exploration

Pulsar navigation is a promising autonomous navigation system for spacecraft, which is applicable to the entire solar system. However, the pulsar’s directional error and the onboard clock error are two types of systematic errors that seriously reduce navigation accuracy. To solve this problem, a star angle/double-differenced pulse time of arrival(SA/DDTOA) integrated navigation method is proposed. Since measurements obtained by observing different pulsars contain the same clock errors, the measurements can be differed to eliminate the common clock error. Then, the pulsar-differenced measurements at neighbor filtering time can be differed to suppress the effect of the pulsar’s directional error on navigation precision. Star angle is used to obtain absolute navigation information, which denotes the angles between the light of sight of Jupiter and that of its background stars. Simulation results demonstrate that the proposed method can eliminate the influence of the onboard clock error and greatly weaken the effects of the pulsar’s directional error. The navigation accuracy is better than the traditional star angle/pulse time of arrival integrated navigation method and star angle/pulse time difference of arrival integrated navigation method. In addition, the navigation accuracy of the SA/DDTOA integrated navigation method is less affected by Jupiter’s ephemeris error. This work greatly reduces the influence of common systematic errors in pulsar navigation on navigation accuracy.

Precision agriculture intelligent connection network based on visual navigation

AbstractIn the process of contemporary agricultural production, the application of intelligent connection and automatic navigation technology of modern agricultural machinery is increasingly common. The application of visual navigation technology in the field of intelligent connection of agricultural machines can provide accurate and reliable positioning signals for agricultural machines so as to carry out intelligent and automatic agricultural machinery operations. In order to realise precision agricultural operation and intelligent navigation, a network of combining decision‐making by using visual navigation recognition and image processing is proposed. The system design mainly includes two parts: visual navigation system and control network. Agricultural machinery uses monocular vision image acquisition to collect farmland image. The computer analyzes the image, extracts the navigation centreline, obtains the navigation path information, and completes the identification and positioning of the navigation path. The simulation process verifies that the designed image navigation precision agricultural intelligent connection network can provide efficient, precise, and intelligent control of agricultural machinery.

Development of display and disposal work system for convenience stores using dual-arm robot

ABSTRACT Labor shortages have been reported in numerous developed economies, and they are becoming increasingly severe, particularly in convenience stores. An important task in a convenience store is the display and disposal of a wide variety of products. However, there are technological gaps between the present technology and the expected setup. To solve the demand for the display and disposal tasks in convenience stores, ‘World Robot Summit Future Convenience Store Challenge(WRS FCSC)’ was established in 2017. The tasks of WRS FCSC are important; however, it is difficult to completely achieve the required tasks because the tasks in convenience stores are optimized for human workers. In this study, we first analyze the functions required to solve the problems of tasks in WRS FCSC. Through the analysis, we have developed several system components including two kinds of grippers for dual-arm and its manipulation strategy, intelligent shelf with manipulation support function and human detection function surrounding the shelf, object pose detection algorithm, precise autonomous navigation strategy, and management system to integrate the all the components. The effectiveness of the developed system is evaluated through the same scenario of WRS FCSC. Through the experiment, we confirmed the effectiveness our developed system.

Adapting Neural-Based Models for Position Error Compensation in Robotic Catheter Systems

Robotic catheter systems with master–slave designs are employed for teleoperated navigation of flexible endovascular tools for treating calcified lesions. Despite improved tool manipulation techniques, patient safety and lowering operative risks remain top priorities. Therefore, minimizing undesirable drifts and imprecise navigation of flexible tools during intravascular catheterization is essential. In the current master–slave designs, finite displacement lag between position command and actual navigation action at the slave device affects smooth catheterization. In this study, we designed and developed a compact 2-DOF robotic catheter system and characterized the influence of displacement step values, velocity, and motion gap on the position error at the slave device. For uniform and varying motion commands from the master platform, the results indicate that the overall position error increases with the distance traveled and the displacement step values, respectively. Hence, we proposed using recurrent neural networks—long short-term memory and gated recurrent unit controllers to predict the slave robot’s position and appropriate compensation value per translation step. An analysis of in-silico studies with CoppeliaSim showed that the neural-based controllers can ensure uniform motion mapping between the master–slave devices. Furthermore, we implemented the models within the RCS for a catheterization length of 120 mm. The result demonstrates that the controllers suitably aid the slave robot’s stepwise displacement. Thus, the neural-based controllers help match the translational motion and precise tool navigation by the slave robotic device. Therefore, the neural-based controllers could contribute to alleviating patients’ safety concerns during robotic interventions.

Precise real-time navigation of the small TJU-1 satellite using GPS, GLONASS and BDS

Precise real-time navigation of the small Tianjin University-1 (TJU-1) satellite based on GPS, GLONASS, BDS-2 and BDS-3 is studied in this contribution for the first time. Experimental results showed that (1) the real-time position accuracies could reach the level of 0.6 ∼ 0.8 m, 0.9 ∼ 1.1 m, 2.0 ∼ 3.5 m and 0.18 ∼ 0.22 m, respectively, for the solutions based on single GPS, GLONASS, BDS-2 and BDS-3; (2) The multi-GNSS fusion could significantly improve the accuracy, reliability and availability of real-time navigation compared with the single-GNSS solution especially when the observation conditions of a single-GNSS are poor or over some anomalous orbit arcs where only few or even no observations are available; (3) a remarkable real-time orbit accuracy of 0.187 m for 3D position is achieved based on BDS-2 + BDS-3 fusion. These accuracy performances have fully demonstrated that, precise real-time navigation based on multi-GNSS measurements would be a very attractive technique for broad small LEO satellite applications requiring the high-precision real-time orbits.

Spontaneous symmetry breaking propulsion of chemically coated magnetic microparticles

Chemically coated micro/nanoparticles are often used in medicine to enhance drug delivery and increase drug up-take into specific areas of the body. Using a recently discovered spontaneous symmetry breaking propulsion mechanism, we demonstrate that chemically coated microparticles can swim through mucus solution under precise navigation and that certain functionalizations can dynamically change propulsion behavior. For this investigation biotin, Bitotin-PEG3-amine, and biotin chitosan were chemically functionalized onto the surfaces of magnetic microparticles using an avidin–biotin complex. These chemicals were chosen because they are used prolifically in drug delivery applications, with PEG and chitosan having well known mucoadhesive effects. Coated microparticles were then suspended in mucus synthesized from porcine stomach mucins and propelled using rotating magnetic fields. The relationship between different chemical coatings, microparticle velocity, and controllability were thoroughly explored and discussed. Results indicate that the biotinylated surface coatings altered the propulsion behavior of microparticles, with performance differences interlinked to both magnetic field properties and localized mucus properties. Precisely controlled drug carrying microparticles are envisioned to help supplant traditional drug delivery methods and enhance existing medical techniques utilizing micro/nanoparticles.

Application of Distributed wMPS System in AGV Automatic Navigation

AGV vehicles play an important role in industrial automation production, and automatic navigation is the basis for automatic operation of AGV vehicles. In order to improve the navigation accuracy of AGV vehicles, based on the workspace measuring and positioning system (wMPS), this paper proposes to use upright and top mounted transmitter stations to build precision coordinate measuring fields, and analyzes the construction process and layout optimization principle of precision measuring fields in detail, Finally, the measurement accuracy of the coordinate field is illustrated through experiments. The experimental results show that the comparison error between the precision coordinate measurement field constructed by the method proposed in this paper and the measurement results of the tracker is controlled below 0.2 mm, which can effectively ensure the measurement accuracy and realize the automatic precision navigation of AGV vehicles.

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation.

Automated robots are an important part of realizing sustainable food production in smart agriculture. Agricultural robots require a powerful and precise navigation system to be able to perform tasks in the field. Aiming at the problems of complex image background, as well as weed and light interference factors of the visual navigation system in field and greenhouse environments, a Faster-U-net model that retains the advantages of the U-net model feature jump connection is proposed. Based on the U-net model, pruning and optimization were carried out to predict crop ridges. Firstly, a corn dataset was trained to obtain the weight of the corn dataset. Then, the training weight of the obtained corn dataset was used as the pretraining weight for the cucumber, wheat, and tomato datasets, respectively. The three datasets were trained separately. Finally, the navigation line between ridges and the yaw angle of the robot were generated by B-spline curve fitting. The experimental results showed that the parameters of the improved path segmentation model were reduced by 65.86%, and the mPA was 97.39%. The recognition accuracy MIoU of the Faster-U-net model for maize, tomatoes, cucumbers, and wheat was 93.86%, 94.01%, 93.14%, and 89.10%, respectively. The processing speed of the single-core CPU was 22.32 fps/s. The proposed method had strong robustness in predicting rows of different crops. The average angle difference of the navigation line under a ridge environment such as that for corn, tomatoes, cucumbers, or wheat was 0.624°, 0.556°, 0.526°, and 0.999°, respectively. This research can provide technical support and reference for the research and development of intelligent agricultural robot navigation equipment in the field.

On-board BDS dynamic filtering ballistic determination and precision evaluation

Abstract BeiDou navigation satellite system (BDS) receivers on-board cannot give real-time positioning precision in tracking missile and carrier rocket. In this article, the dynamic filtered optimal estimation theory is applied to the highly dynamic on-board BDS ballistic parameter solution, an optimally constrained geometric dilution precision (GDOP) constellation selection strategy is proposed, a mathematical model based on dynamic filtered estimation for the ballistic determination and precision estimation of the on-board BDS is established, and an analysis of the on-board BDS positioning precision is carried out using the observed data. The calculation results show that the dynamic filter positioning algorithm is simple, practical, and reliable, which can effectively suppress and reduce the random errors of ballistic parameters and significantly improve the positioning precision, fully satisfying the needs of high dynamic and high precision navigation and positioning users, and has good application prospects.

Continuous and Precise Positioning in Urban Environments by Tightly Coupled Integration of GNSS, INS and Vision

Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenarios. To fill this gap, this letter proposes to exploit the complementariness of the global navigation satellite system (GNSS), inertial measurement unit (IMU) and vision via a tightly coupled integration method, aiming to achieve continuous and accurate navigation in urban environments. Specifically, the raw GNSS carrier phase and pseudorange measurements, IMU data, and visual features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF) to make full use of the multi-sensor information and reject potential outlier measurements. Furthermore, the widely used high-precision GNSS models including precise point positioning (PPP) and real-time kinematic (RTK) are unified in the proposed integrated system to increase usability and flexibility. We validate the performance of the proposed method on several challenging datasets collected in urban canyons and compare against the loosely coupled and state-of-the-art methods.

AUV-Assisted Diver Navigation

This letter presents a precise navigation technique for a scuba diver using an autonomous underwater vehicle (AUV) as a positioning aid. We develop and evaluate the state estimation algorithms and communication architecture for diver navigation based on subsurface human-AUV teaming with no requirement for ocean current data or exact diver speeds. By depending on acoustic communication and commercial AUV navigation capabilities, our method provides a unique capability for localizing a diver's position during an extended mission, without the requirement to maintain visual contact with the diver during the mission, or to deploy acoustic beacons. We utilize the Woods Hole Oceanographic Institution Micromodem 2 for range-only single-beacon navigation between two kayaks serving as proxies for the diver and REMUS 100 AUV. Range and odometry measurements are fused in a factor graph using incremental smoothing and mapping 2 (iSAM2) with appropriate motion and measurement models to provide real-time diver position estimates given unknown ocean currents. Field experiments demonstrate an average online endpoint error of 4.53 meters after 400-meter transits.

Design and Validation of Accuracy Assessment Tools and Algorithms for Optical Positioning System in Surgical Navigation

The precision of optical positioning system is one of the most important factors which affects the precision of navigation guided surgery. In this study, an efficient and low-cost tool and its algorithm were proposed to evaluate the accuracy of optical positioning system based on the ablation scenario of liver cancer, and two validation experiments were designed. Experimental results show that the tool and its algorithm can evaluate the accuracy of the current positioning system accurately and efficiently.

무인 이동체를 위한 LiDAR 오차 모델링 및 가혹 환경 시뮬레이터 개발

Unmanned aerial vehicles (UAVs) have received attention as aircrafts that can perform missions on behalf of humans in vulnerable environments, such as for military purposes. Therefore, research on navigation systems, one of the underlying technologies for realizing UAVs, is actively being conducted. However, in an access-vulnerable environment, a global navigation satellite system alone is insufficient for autonomous driving and for ensuring stability. Accordingly, auxiliary sensors have been used as a navigation solution in many studies; for precise navigation using LiDAR, stability and accuracy are verified in relation to the performance analysis of the algorithm and environmental factors. However, data collection is expensive and requires a considerable amount of time, leading to operational difficulties. This problem can be resolved using a LiDAR simulator, but previously proposed simulators have limitations in terms of their development effectiveness owing to the differences between actual data. Therefore, we reviewed literature on the error analysis of the LiDAR measurement value. We integrated the LiDAR error models analyzed in previous research and synthesized and implemented the error parameters of a specific LiDAR model, Ouster OS1-32, through a simulator.

Multi-GNSS PPP/INS/Vision/LiDAR tightly integrated system for precise navigation in urban environments

Nowadays, high-precision navigation is a fundamental prerequisite for autonomous driving technology. However, it is often difficult for a stand-alone sensor to meet the needs of high-precision navigation in complex scenarios. To address this problem, we propose a tightly coupled precise point positioning (PPP)/inertial navigation system (INS)/Vision/LiDAR integration method to achieve high-precision, continuous and reliable navigation in urban environments. The multi-GNSS carrier phase and pseudorange measurements, low-cost microelectromechanical system (MEMS) inertial measurement units (IMU) records, sparse visual landmarks and extracted LiDAR edge/planar features are directly fused at the observation level through a centralized Extended Kalman Filter (EKF). The vehicle experiments in different GNSS availability conditions were conducted to evaluate the proposed method. Results indicate that our proposed PPP/INS/Vision/LiDAR integration can maintain sub-meter level positioning in both GNSS half-open-sky and difficult environments, with improvements of (50.7%, 58.6%, 54.3%) and (46.2%, 55.0%, 58.8%) relative to PPP/INS/Vision and PPP/INS/LiDAR, respectively. Moreover, both visual and LiDAR information can significantly improve the velocity and attitude estimation performance, especially for the heading.

Evaluation of Robotic-Assisted Carotid Artery Stenting in a Virtual Model Using Motion-Based Performance Metrics

Purpose: Robotic-assisted carotid artery stenting (CAS) cases have been demonstrated with promising results. However, no quantitative measurements have been made to compare manual with robotic-assisted CAS. This study aims to quantify surgical performance using tool tip kinematic data and metrics of precision during CAS with manual and robotic control in an ex vivo model. Materials and Methods: Transfemoral CAS cases were performed in a high-fidelity endovascular simulator. Participants completed cases with manual and robotic techniques in 2 different carotid anatomies in random order. C-arm angulations, table position, and endovascular devices were standardized. Endovascular tool tip kinematic data were extracted. We calculated the spectral arc length (SPARC), average velocity, and idle time during navigation in the common carotid artery and lesion crossing. Procedural time, fluoroscopy time, movements of the deployed filter wire, precision of stent, and balloon positioning were recorded. Data were analyzed and compared between the 2 modalities. Results: Ten participants performed 40 CAS cases with a procedural success of 100% and 0% residual stenosis. The median procedural time was significantly higher during the robotic-assisted cases (seconds, median [interquartile range, IQR]: 128 [49.5] and 161.5 [62.5], p=0.02). Fluoroscopy time differed significantly between manual and robotic-assisted procedures (seconds, median [IQR]: 81.5 [32] and 98.5 [39.5], p=0.1). Movement of the deployed filter wire did not show significant difference between manual and robotic interventions (mm, median [IQR]: 13 [10.5] and 12.5 [11], p=0.5). The postdilation balloon exceeded the margin of the stent with a median of 2 [1] mm in both groups. Navigation with robotic assistance showed significantly lower SPARC values (–5.78±3.14 and –8.63±3.98, p=0.04) and higher idle time values (8.92±8.71 and 3.47±3.9, p=0.02) than those performed manually. Conclusions: Robotic-assisted and manual CAS cases are comparable in the precision of stent and balloon positioning. Navigation in the carotid artery is associated with smoother motion and higher idle time values. These findings highlight the accuracy and the motion stabilizing capability of the endovascular robotic system. Clinical Impact Robotic assistance in the treatment of peripheral vascular disease is an emerging field and may be a tool for radiation protection and the geographic distribution of endovascular interventions in the future. This preclinical study compares the characteristics of manual and robotic-assisted carotid stenting (CAS). Our results highlight, that robotic-assisted CAS is associated with precise navigation and device positioning, and smoother navigation compared to manual CAS.

Semi-Direct Point-Line Visual Inertial Odometry for MAVs

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a novel semi-direct point-line visual inertial odometry (SDPL-VIO) has been proposed for MAVs. In the front-end, point and line features are introduced to enhance image constraints and increase environmental adaptability. At the same time, the semi-direct method combined with IMU pre-integration is used to complete motion estimation. This hybrid strategy combines the accuracy and loop closure detection performance of the feature-based method with the rapidity of the direct method, and tracks keyframes and non-keyframes, respectively. In the back-end, the sliding window mechanism is adopted to limit the computation, while the improved marginalization method is used to decompose the high-dimensional matrix corresponding to the cost function to reduce the computational complexity in the optimization process. The comparison results in the EuRoC datasets demonstrate that SDPL-VIO performs better than the other state-of-the-art visual inertial odometry (VIO) methods, especially in terms of accuracy and real-time performance.