Navigation Performance Research Articles

Enhancing Vehicle Navigation and Safety through Integration of Pre-Recorded Maps with Vehicle Control Unit

Powerful navigation and safety systems are vital to ensuring autonomous (DV) or semi-autonomous vehicles continue well in varied environmental conditions. However, in such a world those who depend on that internet connectivity to navigate their car would have problems in areas where network reliability is less than perfect. The purpose of this work is to investigate the feasibility and advantages of combining offline mapping with locally sensed positioning systems for better vehicle navigation and safety. While connected to the internet, vehicles cache offline maps and use local sensor information (such as GPS) from their inertial navigation system or computer vision without needing continuous access. The research outlines a hardware-software architecture with embedded offline map data storage, edge-based road following algorithms and an integration mechanism to advanced driving assistance systems (ADAS). In the evaluation of performance, accuracy assessments with online mapping systems are considered for offline maps and how these can have implications on end driver usability as well impacts on real-world autonomous driving technologies. The results suggest that offline mapping and on-board sensor-based localization would be able to improve vehicle contextual navigation performance in diverse driving scenarios.

A Review of Research on Marine Main Propulsion Systems

Ship power, as an essential component of ship engineering, directly affects the navigation performance, economy, and environmental friendliness of ships. This article reviews the development history of ship power, introduces the main propulsion devices including internal combustion engines, steam engines, and electric motors, and discusses the structure and optimization design of ship power systems. Modern ship power technology includes traditional diesel and gasoline engines, as well as new power devices such as liquefied natural gas (LNG) engines, hybrid power systems, and fuel cells. Electric ships are gaining attention due to their efficiency and environmental benefits. In the future, ship power technology will develop towards green environmental protection, intelligence, integration, diversification, and digitization, providing more reliable and efficient power support for the shipping industry and the advancement of ship engineering.

Heart rate variability modulates memory function in a virtual task

Heart rate variability (HRV) is considered one of the most relevant indicators of physical well-being and relevant biomarker for preventing cardiovascular risks. More recently, a growing amount of research has tracked an association between HRV and cognitive functions (i.e., attention). Research is still scarce on spatial orientation, a basic capability in our daily lives. It is also an important indicator of memory performance, and its malfunctioning working as an early sign of dementia. In this study, a total of 43 female students (M Age = 18.76; SD = 2.02) were measured in their lnRMSSD using the photoplethysmography technique with the Welltory smartphone app. They were also tested in their spatial memory with The Boxes Room, a virtual navigation test. Measures of physical activity were obtained with the International Physical Activity Questionnaire (IPAQ). Correlation analyses and repeated measures ANOVA were performed, comparing participants with high / low lnRMSSD in their spatial performance. Results showed that, at an equal level of physical activity, participants with a higher lnRMSSD were more effective in the early trials of The Boxes Room, being more precise in estimating the correct position of the stimuli. Moreover, a subsequent simple linear regression showed that a higher lnRMSSD was related to a smaller number of errors at the beginning of the spatial task. Overly, these results outline the relationship between HRV and navigation performance in early stages of processing, where the environment is still unknown and the situation is more demanding.

A hybrid optimization method based on extreme learning machine aided factor graph for INS/GPS information fusion during GPS outages

To enhance the navigation capability of the microelectromechanical system (MEMS)-based inertial navigation system (INS)/global positioning system (GPS) integrated system under satellite denied, a hybrid optimization navigation method using minimal learning parameter (MLP) to improve extreme learning machine (ELM) aided adaptive factor graph (AFG) is proposed. The proposed method mainly contains two innovative optimizations: (1) Fully considering the key parameters of each sensor in the INS/GPS integrated navigation system, the factor graph technology is introduced to develop an information fusion model for the system. Based on the maximum correlation entropy criterion and adaptive technology, the existing factor graph method for information fusion is optimized to solve the problem that the abnormal output of each sensor in the INS/GPS integrated system caused by complex environments affects the accuracy of information fusion, thus enhancing the robustness and navigation performance of the INS/GPS integrated system; (2) Moreover, ELM is optimized by MLP to predict the velocity and position observation information of the INS/GPS integrated system and tackle the performance deterioration of the system during GPS outages. The structural parameters of ELM are optimized with the MLP method, aiming to abate the computational burden of the traditional neural network (NN) to avoid “dimension explosion”, as well as enhance the generalization ability and robustness of the network to make it learn from the uncertainty of the system smoothly and quickly. Relevant ground vehicle experiments are designed to evaluate the proposed method. The experimental results demonstrate that the proposed strategy has superior performance and is more feasible for real-time implementation than other comparison approaches.

Wayfinding in pairs: comparing the planning and navigation performance of dyads and individuals in a real-world environment

Navigation is essential to life, and it is cognitively complex, drawing on abilities such as prospective and situated planning, spatial memory, location recognition, and real-time decision-making. In many cases, day-to-day navigation is embedded in a social context where cognition and behavior are shaped by others, but the great majority of existing research in spatial cognition has focused on individuals. The two studies we report here contribute to our understanding of social wayfinding, assessing the performance of paired and individual navigators on a real-world wayfinding task in which they were instructed to minimize time and distance traveled. In the first study, we recruited 30 pairs of friends (familiar dyads); in the second, we recruited 30 solo participants (individuals). We compare the two studies to the results of an earlier study of 30 pairs of strangers (unfamiliar dyads). We draw out differences in performance with respect to spatial, social, and cognitive considerations. Of the three conditions, solo participants were least successful in reaching the destination accurately on their initial attempt. Friends traveled more efficiently than either strangers or individuals. Working with a partner also appeared to lend confidence to wayfinders: dyads of either familiarity type were more persistent than individuals in the navigation task, even after encountering challenges or making incorrect attempts. Route selection was additionally impacted by route complexity and unfamiliarity with the study area. Navigators explicitly used ease of remembering as a planning criterion, and the resulting differences in route complexity likely influenced success during enacted navigation.

Mobile Robot Navigation Based on Noisy N-Step Dueling Double Deep Q-Network and Prioritized Experience Replay

Effective real-time autonomous navigation for mobile robots in static and dynamic environments has become a challenging and active research topic. Although the simultaneous localization and mapping (SLAM) algorithm offers a solution, it often heavily relies on complex global and local maps, resulting in significant computational demands, slower convergence rates, and prolonged training times. In response to these challenges, this paper presents a novel algorithm called PER-n2D3QN, which integrates prioritized experience replay, a noisy network with factorized Gaussian noise, n-step learning, and a dueling structure into a double deep Q-network. This combination enhances the efficiency of experience replay, facilitates exploration, and provides more accurate Q-value estimates, thereby significantly improving the performance of autonomous navigation for mobile robots. To further bolster the stability and robustness, meaningful improvements, such as target “soft” updates and the gradient clipping mechanism, are employed. Additionally, a novel and powerful target-oriented reshaping reward function is designed to expedite learning. The proposed model is validated through extensive experiments using the robot operating system (ROS) and Gazebo simulation environment. Furthermore, to more specifically reflect the complexity of the simulation environment, this paper presents a quantitative analysis of the simulation environment. The experimental results demonstrate that PER-n2D3QN exhibits heightened accuracy, accelerated convergence rates, and enhanced robustness in both static and dynamic scenarios.

An improved crater-based navigation approach using projective invariants

This research presents an innovative approach designed to enhance the performance of crater-based navigation systems. The core of this approach revolves around proposing a novel method for calculating and incorporating the degree of perturbation observed in matched craters. The foundation of our algorithm lies in the concept of comparing the similarity of projective invariants between image and database craters during the crater matching process. The degree of perturbation in each extracted crater is quantified and normalized using a multivariate Gaussian model. These values are subsequently employed as observation weights within the navigation system. Simulation results confirm the effectiveness of our approach in accurately computing weights that reflect the varying levels of error between craters. Moreover, when integrated into the navigation system, proposed method substantially elevates navigation performance.

What Prevents Us From Learning Virtual Reality Space: A Study of Indoor Corridor Angle and Obstacle Transparency

People sometimes become disorientated and feel unnatural in virtual reality (VR) spaces created via devices such as head-mounted displays. The main problem is that multiple structures make it difficult to learn the VR space, particularly indoors. These structures segment the space and form boundaries for sightlines and activity areas. How obstacles in segmented space affect human behavior have been less discussed in studies related to navigation. This study sought to clarify the mechanism for the effect of spatial segmentation effect on human spatial cognition, in the context of indoor navigation. This study was conducted in a VR scenario with an omni-directional treadmill; a total of 76 college students participated, and 52 of them were included in the analysis. The study found that the cognitive distortion from spatial segmentation primarily operates by activity area rather than visual obstruction, and it can be inferred that the basis of human abstraction of spatial information may perhaps be the activity area. Regardless of obstacle transparency, non-rectangular spatial segmentation deteriorates navigational performance. The results suggest that, future metaverse communities should consider designing rectangular segmented activity areas to improve navigation performance and reduce disorientation.

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

Application Research of 5G Communication Technology in Satellite Navigation and Positioning

With the rapid development of 5G communication technology and the increasing perfection of global satellite navigation and positioning, the application of 5G communication technology in satellite navigation and positioning is particularly important. Based on the overview of 5G communication technology and satellite navigation and positioning, this paper analyzes the important value of 5G communication technology in satellite navigation and positioning, and then discusses the application of 5G communication technology and satellite navigation and positioning, aiming to integrate 5G communication technology, further improve the performance of satellite navigation and positioning system, and expand its application scenarios. It will bring greater convenience and benefits to social development.

Positioning of a lunar surface rover on the south pole using LCNS and DEMs

With the renewed interest in the lunar surface exploration, the European Space Agency is supporting, as part of the Moonlight program, the development of a dedicated Lunar Communications and Navigation Service (LCNS) infrastructure. This should enable to achieve real time positioning and autonomous navigation in the cislunar space, including, among others, lunar landing and surface operations. One of the services proposed, as part of the LunaNet framework (NASA, 2022), is called the Lunar Augmented Navigation Service (LANS) and it is based on a GNSS-like concept. However, compared to Earth-based GNSS, the expected number of available satellites in lunar orbit will initially be limited, which triggers the interest of coupling these technologies at the receiver level with other navigation data sources. In the case of surface rovers, use of digital elevation model (DEM) data has shown to be a potential other source of information to support positioning. The purpose of this contribution is to look at the actual navigation performances achievable when using a simple (four satellites constellation) lunar radio navigation constellation combined with a DEM, through a state estimation filter, for different rover dynamic and different DEM accuracy levels. It is shown that a 5 meter real-time horizontal position accuracy can be achieved, for 99.7% of the time, using a precise enough DEM (5 m/pixel) and a 4 satellite-constellation under different rover’s dynamic conditions (i.e. velocities).

Spreading code optimization for low-earth orbit satellites via mixed-integer convex programming

Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems. By improving the codes’ correlation sidelobes, we can enhance navigation performance while minimizing the required spreading code lengths. In the case of low-earth orbit (LEO) satellite navigation, shorter code lengths (on the order of a hundred) are preferred due to their ability to achieve fast signal acquisition. Additionally, the relatively high signal-to-noise ratio in LEO systems reduces the need for longer spreading codes to mitigate inter-channel interference. In this work, we propose a two-stage block coordinate descent (BCD) method which optimizes the codes’ correlation properties while enforcing the autocorrelation sidelobe zero property. In each iteration of the BCD method, we solve a mixed-integer convex program over a block of 25 binary variables. Our method is applicable to spreading code families of arbitrary sizes and lengths, and we demonstrate its effectiveness for a problem with 66 length-127 codes and a problem with 130 length-257 codes.

Graph neural network based abnormal perception information reconstruction and robust autonomous navigation

PurposeThis paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.Design/methodology/approachIn this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.FindingsComparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.Originality/valueReliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

The application of CNN in Smart city management and supervision under the background of Smart city

Intelligent transportation management, as an important component of intelligent city supervision, has practical value in improving the effectiveness of urban management. To improve the efficiency of intelligent city traffic management, this study proposes to use convolutional neural networks to improve the performance of navigation and intelligent transportation networks in intelligent transportation. This network is used for vehicle recognition in navigation to predict road congestion and determine the optimal driving path. This structure is applied in the intelligent road network to monitor the driving status of vehicles on the road. It is combined with the vehicle networking system to locate abnormal vehicles to achieve intelligent traffic management and supervision. These results confirm that this algorithm achieves a vehicle recognition accuracy of 91.2 % after 96 iterations and completes the recognition process within 163 ms. This algorithm converges to a vehicle positioning accuracy of 82.4 % after 93 iterations and achieves positioning within 277 ms. This algorithm can not only improve the optimal path planning effect of navigation, but also identify abnormal vehicles within 77 ms. Therefore, the proposed algorithm has high practical value in intelligent transportation management.

CardioXplorer: An Open-Source Modular Teleoperative Robotic Catheter Ablation System

Atrial fibrillation, the most prevalent cardiac arrhythmia, is treated by catheter ablation to isolate electrical triggers. Clinical trials on robotic catheter systems hold promise for improving the safety and efficacy of the procedure. However, expense and proprietary designs hinder accessibility to such systems. This paper details an open-source, modular, three-degree-of-freedom robotic platform for teleoperating commercial ablation catheters through joystick navigation. We also demonstrate a catheter-agnostic handle interface permitting customization with commercial catheters. Collaborating clinicians performed benchtop targeting trials, comparing manual and robotic catheter navigation performance. The robot reduced task duration by 1.59 s across participants and five trials. Validation through mean motion jerk analysis revealed 35.2% smoother robotic navigation for experts (≥10 years experience) compared to the intermediate group. Yet, both groups achieved smoother robot motion relative to the manual approach, with the experts and intermediates exhibiting 42.2% and 13.6% improvements, respectively. These results highlight the potential of this system for enhancing catheter-based procedures. The source code and designs of CardioXplorer have been made publicly available to lower boundaries and drive innovations that enhance procedure efficacy beyond human capabilities.

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

In civil aviation systems, the inertial measurement unit (IMU)/Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, redundant IMUs/GNSS integrated navigation systems are susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU faults. Additionally, dealing with correlated measurements between redundant constructions presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation systems. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace covariance of the primary filter. An optimal allocation scheme for continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

Mechanism of Speed Loss Reduction and Propulsion Efficiency Improvement of ONR Tumblehome with Active-Controlled Stern Flaps in Resonance Waves

The stern flap is a practical hull appendage equipment that enhances ship navigation performance and saves energy. The existing studies mainly focus on the fixed stern flap, other than an actively controlled one, so it is worth further exploring its effect and mechanism. By implanting the PID controller to the stern flap, this paper proposed a free-running CFD model on the ONRT (the Office of Naval Research Tumblehome) ship coupled with the active-controlled stern flap to investigate the hydrodynamic performance in resonance waves. The free-running performance in calm water and regular waves is numerically researched and verified versus the experimental and referenced results. Then, the effect of different PID coefficients and control strategies of the stern flap on the traveling speed, attitudes, and propulsion performance under the resonance wave condition is conducted, and the influence mechanism is explored. The results show that adopting a fixed flap controller and PID controller can reduce the original speed loss by 4.2% and 6.9%, respectively, and increase the average propulsive efficiency of the propeller by 1.0% and 1.4%, respectively. Further analysis reveals that the global effect of the suppressed motion attitudes due to the installation of the fixed flap effectively contributes to the resistance reduction. However, the local effect of the stern flap increases the resistance due to interaction with the propeller and stern. The PID-controlled stern flap exhibits similar average attitudes compared to the fixed one, which means the resistance reduction of the global effect is kept the same, and the active stern flap further improves the stern flow field, where the resistance increment of the local effect is weakened, enhancing the traveling speed and improving the propulsion efficiency.

The relationship between object-based spatial ability and virtual navigation performance

Spatial navigation is a multi-faceted behaviour drawing on many different aspects of cognition. Visuospatial abilities, such as mental rotation and visuospatial working memory, in particular, may be key factors. A range of tests have been developed to assess visuospatial processing and memory, but how such tests relate to navigation ability remains unclear. This understanding is important to advance tests of navigation for disease monitoring in various disorders (e.g., Alzheimer’s disease) where spatial impairment is an early symptom. Here, we report the use of an established mobile gaming app, Sea Hero Quest (SHQ), as a measure of navigation ability in a sample of young, predominantly female university students (N = 78; 20; female = 74.3%; mean age = 20.33 years). We used three separate tests of navigation embedded in SHQ: wayfinding, path integration and spatial memory in a radial arm maze. In the same participants, we also collected measures of mental rotation (Mental Rotation Test), visuospatial processing (Design Organization Test) and visuospatial working memory (Digital Corsi). We found few strong correlations across our measures. Being good at wayfinding in a virtual navigation test does not mean an individual will also be good at path integration, have a superior memory in a radial arm maze, or rate themself as having a strong sense of direction. However, we observed that participants who were good in the wayfinding task of SHQ tended to perform well on the three visuospatial tasks examined here, and to also use a landmark strategy in the radial maze task. These findings help clarify the associations between different abilities involved in spatial navigation.

SD-SLAM: A semantic SLAM approach for dynamic scenes based on LiDAR point clouds

Point cloud maps generated via LiDAR sensors using extensive remotely sensed data are commonly used by autonomous vehicles and robots for localization and navigation. However, dynamic objects contained in point cloud maps not only downgrade localization accuracy and navigation performance but also jeopardize the map quality. In response to this challenge, we propose in this paper a novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds, referred to as SD-SLAM hereafter. The main contributions of this work are in three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic scenes based on LiDAR point clouds, 2) employing semantics and Kalman filtering to effectively differentiate between dynamic and semi-static landmarks, and 3) making full use of semi-static and pure static landmarks with semantic information in the SD-SLAM process to improve localization and mapping performance. To evaluate the proposed SD-SLAM, tests were conducted using the widely adopted KITTI odometry dataset. Results demonstrate that the proposed SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM, improving vehicle localization and mapping performance in dynamic scenes, and simultaneously constructing a static semantic map with multiple semantic classes for enhanced environment understanding.

Hull form optimization research based on multi‐precision back‐propagation neural network approximation model

AbstractIn order to shorten the optimization cycle of ship design optimization and solve the time‐consuming problem of computational fluid dynamics (CFD) numerical calculation, this paper proposes a multi‐precision back‐propagation neural network (MP‐BP) approximation technology. Fewer high‐precision ship samples and more low‐precision ship samples were used to construct an approximate model, back‐propagation (BP) neural network was used to train multi‐precision samples. So that the approximate model is as close as possible to the real model, and achieving the effect of high‐precision approximation model. Subsequently, numerical verification and typical hull form verification are given. Based on CFD and Rankine theory, the multi‐objective design optimization framework for ship comprehensive navigation performance is constructed. The multi‐objective approximation model of KCS ship is constructed by MP‐BP approximation technology, and optimized by particle swarm optimization (PSO) algorithm. The results show that the multi‐objective optimization design framework using the MP‐BP approximation model can capture the global optimal solution and improve the efficiency of the entire hull form design optimization. It can provide a certain degree of technical support for green ship and low‐carbon shipping.