Navigation Information Research Articles

UUV cluster navigation data extraction fusion positioning algorithm with information geometry

With the rapid development of the ocean economy, underwater unmanned vehicle (UUV) clusters have become the main tool for ocean development, but the complex interference and occlusion in the underwater environment have posed great challenges to the positioning of UUV clusters, becoming a key problem in the application of UUV clusters. To address this issue, this paper proposes a distributed heterogeneous navigation information geometric fusion positioning method for underwater unmanned vehicle clusters based on data extraction(DEFP-IG). This method converts the distributed navigation source information of UUVs into an information geometric probability model, and constructs a factor graph fusion architecture based on data extraction to improve the suppression of interference, thereby achieving high-precision positioning of UUV clusters. Comparing the method proposed in this paper with existing methods in different scenarios, the results show that the proposed method has higher positioning accuracy and good suppression capability for abrupt errors.

Advancements in amphibious robot navigation through wheeled odometer uncertainty extension and distributed information fusion

The advancement and safeguarding of the water-land interface region is of paramount importance, and amphibious robots with the capacity for autonomous operation can play a pivotal role in this domain. However, the inability of the majority of reliable navigation sensors to adapt to the water-land interface environment presents a significant challenge for amphibious robots, as obtaining positional information is crucial for autonomous operation. To address this issue, we have proposed a positioning and navigation framework, designated as NAWR (Navigation Algorithm for Amphibious Wheeled Robots), with the objective of enhancing the navigation capabilities of amphibious robots. Firstly, a method for representing the odometer's confidence based on a simplified wheel-terrain interaction model has been developed. This method quantitatively assesses the reliability of each odometer by estimating the slip rate. Secondly, we have introduced an improved split covariance intersection filter (I-SCIF), which maximizes the utilization of navigation information sources to enhance the accuracy of positional estimation. Finally, we will integrate these two methods to form the NAWR framework and validate the effectiveness of the proposed methods through multiple robot field trials. The results from both field trials and ablation tests collectively demonstrate that the modules and overall approach within the NAWR framework effectively enhance the navigation capabilities of amphibious robots.

Health Librarians' Perceptions and Utilization of ChatGPT for Digital Literacy in Libraries

Libraries are adapting to the digital age, with health librarians utilizing AI technologies like ChatGPT to support patrons' digital literacy (Chen, 2023). Although prior research has explored AI's role in libraries, little is known about how health librarians leverage ChatGPT, especially regarding health information seeking behavior. This study investigates health librarians' experiences and perspectives on integrating ChatGPT into digital literacy services. Through semi-structured interviews, thematic analysis reveals innovative applications of ChatGPT, such as real-time Q&A sessions and personalized learning experiences. Despite technical challenges like accuracy and reliability, librarians implement quality control measures and continuously update ChatGPT's knowledge base. The study revealed ChatGPT's positive impact on patrons, encouraging improved understanding of health information and confidence in digital resource navigation. Despite challenges, health librarians acknowledge ChatGPT's potential to enhance digital literacy services. Recommendations include addressing technical issues, providing librarian training, and improving user engagement strategies (Yamson, 2023). Overall, this research shows ChatGPT's role in advancing digital literacy within libraries, providing perspectives into its effective integration and potential improvements.

Analysis of Alignment Accuracy due to Velocity/Attitude Error of Master Inertial Navigation System in Velocity/Attitude Matching Transfer Alignment

This paper theoretically analyzes the effect of the velocity and attitude errors of Master Inertial Navigation System(MINS) on the accuracy of Slave Inertial Navigation System(SINS) transfer alignment in velocity and attitude matching, and validates the analysis through simulation. Theoretical analysis involves deriving a new state equation that considers the velocity and attitude errors of MINS from the state equation of the transfer alignment filter, and deriving the state estimation equation of the Kalman filter based on this. The analysis confirms that MINS's velocity and attitude errors induce the same level of velocity and attitude errors in SINS. A reference inertial navigation system model is added to the simulation model, and the transfer alignment accuracy is analyzed by comparing the navigation information of MINS and SINS with the reference inertial navigation system. It is confirmed that the accuracy analysis results through simulation are consistent with the theoretically analyzed results, and through this, the validity of the theoretically analysis in this paper is verified. The research findings indicate that when performing transfer alignment using MINS, which is likely to be operated for prolonged periods in pure inertial navigation mode, the navigation errors of MINS are transferred to SINS. This implies that initial correction navigation is necessary to be considered for SINS

Adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination

Abstract The attitude of a vehicle can be largely determined by integrating the global navigation satellite system (GNSS) and inertial measurement unit (IMU) in land-based applications. However, traditional dual-antenna GNSS/IMU integrated systems are susceptible to signal reflection, diffraction, and even interruption, leading to reduced accuracy and reliability in challenging environments. To address this issue, this paper proposes an adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination. First, we propose an IMU-aided baseline length constraint model to support the ambiguity resolution (AR) process. This model incorporates accurate prior information from the IMU, improving the success rate of AR within the dual-antenna GNSS/IMU integrated system. Second, we present an adaptive IMU error correction mechanism based on long short-term memory (LSTM) and particle swarm optimization (PSO) to assist in vehicle attitude determination and mitigate attitude error drift of the lMU during GNSS outages. Field test results verified that, compared to two other candidate algorithms, the proposed algorithm improves accuracy in roll, pitch, and yaw by 19.23%, 30.56%, and 67.12%, respectively, and by 12.50%, 10.71%, and 38.39% respectively. Moreover, in two distinct scenarios where GNSS is blocked for 120 seconds, the proposed algorithm still provides accurate and stable attitude information for vehicle navigation, with the accuracy of roll, pitch, and yaw kept within 0.08 degrees.

Standardized research on multifunctional beacon multi-sensor data acquisition and transmission

Abstract This paper discusses the research on the standardization of data acquisition and transmission for multifunctional beacon systems. The multifunctional beacon system integrates various types of sensing devices and can provide real-time, comprehensive and accurate marine environment and navigation information for ships and shore-based users. However, the system currently faces challenges such as non-uniform data formats and communication protocols, low hardware standardization and integration, and data and network security risks. In order to solve these problems, this paper puts forward the solution of “standard terminal for data acquisition and transmission”, which achieves the compatibility of equipment interface, unified data format, and reliable information security. It lays the foundation for the standardization of multi-purpose beacon system. Through modular design and standardized packaging, the performance, reliability and efficiency of the hardware of multi-purpose beacons are improved, the development and maintenance costs are reduced, and the flexibility of the system is enhanced. The “Standard Terminal for Data Acquisition and Transmission” is applied to the multi-purpose beacon system, and interfaces and integrates with the existing “Operation Management System for Navigation Guarantee” to realize a unified platform for multi-purpose beacons. This research helps to promote the formulation of multi-purpose beacon data format and communication protocol standards at the national or industrial level, provides important reference for the formulation of international standards, and is of great significance in promoting the standardized development and application promotion of multi-purpose beacon technology.

AR for Warehouses: a Marker-based Augmented System for Navigation and Inventory Management

This study investigates using augmented reality (AR) to assist warehouse operators. By employing head-mounted displays (HMDs), the AR application overlays relevant navigational information and inventory data directly onto the user’s field of vision, enabling a hands-free interaction system that creates a more efficient workflow. Incorporating a marker-based AR architecture offers precise item localization and navigation support, addressing common issues such as time-consuming item checks and potential navigational errors while moving in the warehouse. Through the assistance of these processes, the AR system aims to enhance the efficiency and precision of warehouse operations. This research highlights the advantages and potential challenges of implementing AR solutions in industrial settings, emphasizing the need for innovative warehouse logistics and management approaches.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Development of a comprehensive open access "molecules with androgenic activity resource (MAAR)" to facilitate risk assessment of chemicals.

The increasing prevalence of endocrine-disrupting chemicals (EDCs) and their potential adverse effects on human health underscore the necessity for robust tools to assess and manage associated risks. The androgen receptor (AR) is a critical component of the endocrine system, playing a pivotal role in mediating the biological effects of androgens, which are male sex hormones. Exposure to androgen-disrupting chemicals during critical periods of development, such as fetal development or puberty, may result in adverse effects on reproductive health, including altered sexual differentiation, impaired fertility, and an increased risk of reproductive disorders. Therefore, androgenic activity data is critical for chemical risk assessment. A large amount of androgenic data has been generated using various experimental protocols. Moreover, the data are reported in different formats and in diverse sources. To facilitate utilization of androgenic activity data in chemical risk assessment, the Molecules with Androgenic Activity Resource (MAAR) was developed. MAAR is the first open-access platform designed to streamline and enhance the risk assessment of chemicals with androgenic activity. MAAR's development involved the integration of diverse data sources, including data from public databases and mining literature, to establish a reliable and versatile repository. The platform employs a user-friendly interface, enabling efficient navigation and extraction of pertinent information. MAAR is poised to advance chemical risk assessment by offering unprecedented access to information crucial for evaluating the androgenic potential of a wide array of chemicals. The open-access nature of MAAR promotes transparency and collaboration, fostering a collective effort to address the challenges posed by androgenic EDCs.

VLAI: Exploration and Exploitation based on Visual-Language Aligned Information for Robotic Object Goal Navigation

Object Goal Navigation(ObjectNav) is the task that an agent need navigate to an instance of a specific category in an unseen environment through visual observations within limited time steps. This work plays a significant role in enhancing the efficiency of locating specific items in indoor spaces and assisting individuals in completing various tasks, as well as providing support for people with disabilities. To achieve efficient ObjectNav in unfamiliar environments, global perception capabilities, understanding the regularities of space and semantics in the environment layout are significant. In this work, we propose an explicit-prediction method called VLAI that utilizes visual-language alignment information to guide the agent's exploration, unlike previous navigation methods based on frontier potential prediction or egocentric map completion, which only leverage visual observations to construct semantic maps, thus failing to help the agent develop a better global perception. Specifically, when predicting long-term goals, we retrieve previously saved visual observations to obtain visual information around the frontiers based on their position on the incrementally built incomplete semantic map. Then, we apply our designed Chat Describer to this visual information to obtain detailed frontier object descriptions. The Chat Describer, a novel automatic-questioning approach deployed in Visual-to-Language, is composed of Large Language Model(LLM) and the visual-to-language model(VLM), which has visual question-answering functionality. In addition, we also obtain the semantic similarity of target object and frontier object categories. Ultimately, by combining the semantic similarity and the boundary descriptions, the agent can predict the long-term goals more accurately. Our experiments on the Gibson and HM3D datasets reveal that our VLAI approach yields significantly better results compared to earlier methods. The code is released athttps://github.com/31539lab/VLAI.

The Effect of Dynamic Effects and Color Transparency of AR-HUD Navigation Graphics on Driving Behavior Regarding Inattentional Blindness

In-vehicle Augmented Reality Head-Up Displays (AR-HUDs) offer drivers integrated navigation and vehicle status information, thereby improving the driving experience. Many studies explored the effects of AR-HUD navigation design and information presentation on driving performance; however, these studies neglected the occurrence of inattentional blindness, which may lead to potential safety hazards. This study used a two-factor within-subjects design to investigate how dynamic effects (pulsation, blinking, movement) and color transparency levels (T1, T0.75, T0.5, T0.35) of AR-HUD navigation graphics influence inattentional blindness and user experience. The results indicated that color transparency significantly affects the drivers’ inattentional blindness behavior and user experience. Specifically, T0.75 resulted in the lowest inattentional blindness, while T0.5 was associated with the shortest reaction time (without blindness) and highest user satisfaction. Dynamic effects did not significantly impact inattentional blindness, but blinking reduced reaction time (without blindness). This study provides design insights for vehicle AR-HUD navigation interfaces.

A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.

Virtual Waiting Room: The New Narrative of Waiting in Oncology Care.

This conceptual study introduces the "virtual waiting room," an innovative, interactive, web-based platform designed to enhance the waiting experience in oncology by providing personalized, educational, and supportive content. Central to our study is the implementation of the circular entry model, which allows for non-linear navigation of health information, empowering patients to access content based on their immediate needs and interests. This approach respects the individual journeys of patients, acknowledging the diverse pathways through which they seek understanding and manage their health. The virtual waiting room is designed not only to support patients but also to facilitate stronger communication and shared understanding between patients, caregivers, and families. By providing a shared digital space, the platform enables caregivers and family members to access the same information and resources, thereby promoting transparency and collective knowledge. This shared access is crucial in managing the emotional complexities of oncology care, where effective communication can significantly impact treatment outcomes and patient well-being. Furthermore, the study explores how the circular entry model within the virtual waiting room can enhance patient autonomy and engagement by offering customized interactions based on user feedback and preferences. This personalized approach aims to reduce anxiety, improve health literacy, and prepare patients more effectively for clinical interactions. By transforming passive waiting into active engagement, the virtual waiting room turns waiting time into a meaningful, informative period that supports both the psychological and informational needs of patients and their support networks.

Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages

NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.

Enhancing Accessibility: A Heuristic Evaluation of Social Presence Interface Design for E-Resources in University Libraries

In this preliminary investigation, a discerning evaluation reveals that users face pervasive challenges accessing e-resources within university libraries. A social presence interface design for e-resources is introduced, recognizing the need for user support through effective communication with librarians and assistance for swift information navigation and system function. This research presents a meticulous usability assessment grounded in heuristic evaluation, engaging six expert evaluators comprising specialists in human-computer interaction and librarians. The primary objective is to ascertain the intuitiveness of the interface design and identify potential issues through expert scrutiny. The study unveiled compelling insights by employing a tailored usability heuristic questionnaire encompassing 39 questions aligned with the developed design. Notably, 75.54% of evaluators agreed unanimously regarding the design’s adherence to heuristic principles, indicating their concurrence with the established criteria. Additionally, evaluators provided valuable commentary and feedback focusing on aspects related to icons, labels, and the efficacy of the help function. This comprehensive evaluation informs and enhances the ongoing discourse surrounding optimal user interface design for e-resources in university library settings.

Reliable measurement selection mechanism-based tightly coupled inertial/bionic polarization integration with position correction

Tightly coupled SINS/BPNS (strapdown inertial navigation system/bionic polarization navigation system) integration has been proven to be a promising navigation tactic to substitute SINS/GNSS (global navigation satellite system) integration in GNSS-denied environments. However, the existing tightly coupled SINS/BPNS integrations lack SINS position correction and the reliable screening of BPNS measurements, leading to difficulty to complete navigation tasks. This paper presents an enhanced tightly coupled SINS/BPNS integration to correct both SINS position and attitude and conduct reliable measurement screening for BPNS. This method establishes a new measurement model by formulating the relationship between the SINS position error and angle of E-vector to effectively correct the SINS position. Based on the corrected SINS position, the solar position is calculated and further plugged in the measurement model, leading to improved accuracy. Further, based on the focal plane polarization camera, a mechanism of reliable measurement selection with two-level processing is developed and combined with the extended Kalman filter to improve the filtering robustness for tightly coupled SINS/BPNS integration. Results of simulations and experiments show that the proposed method not only can effectively correct SINS navigation information, but also can possess a strong robustness to exclude unreliable BPNS measurements, leading to enhanced navigation performance for tightly coupled SINS/BPNS integration.

Interactive design of new media images and architectural space: taking Hangzhou as an example

ABSTRACT Traditional architectural space design often relies on fixed display boards, paper guides, and static displays. Viewers can only passively receive information and lack opportunities to interact with the displayed content, making it difficult to generate deep participation and personalized experiences. This article applied new media images into the interactive design of architectural spaces, transforming and innovating traditional architectural spaces, and providing audiences with a richer and more attractive spatial experience. The navigation service was set as the architectural space design goal, and analyzed using the Leifeng Tower building in Hangzhou as an example. Building Information Modeling (BIM) was used to digitally model the Leifeng Tower building, accurately simulating the building structure, spatial layout, and landscape information. By adding virtual information annotations and navigation routes, tourists were provided with a more diverse and colorful architectural space experience. The experimental results showed that among 400 tourists, the highest number of tourists using augmented reality navigation was 204, and the average satisfaction score of tourists with augmented reality navigation reached 90. Moreover, presenting the guide content in a multimodal form can better enhance the spatial experience of tourists. The use of augmented reality in architectural spatial interaction design can enrich tourists’ perception and experience in the architectural space, enabling them to interact with the building in a more vivid and interactive way.

Seeing the Forest but Naming the Trees: An Object-Over-Place Bias in Learning Noun Labels.

Objects and places are foundational spatial domains represented in human symbolic expressions, like drawings, which show a prioritization of depicting small-scale object-shape information over the large-scale navigable place information in which objects are situated. Is there a similar object-over-place bias in language? Across six experiments, adults and 3- to 4-year-old children were asked either to extend a novel noun in a labeling phrase, to extend a novel noun in a prepositional phrase, or to simply match pictures. To dissociate specific object and place information from more general figure and ground information, participants either saw scenes with both place information (a room) and object information (a block in the room), or scenes with two kinds of object information that matched the figure-ground relations of the room and block by presenting an open container with a smaller block inside. While adults showed a specific object-over-place bias in both extending novel noun labels and matching, they did not show this bias in extending novel nouns following prepositions. Young children showed this bias in extending novel noun labels only. Spatial domains may thus confer specific and foundational biases for word learning that may change through development in a way that is similar to that of other word-learning biases about objects, like the shape bias. These results expand the symbolic scope of prior studies on object biases in drawing to object biases in language, and they expand the spatial domains of prior studies characterizing the language of objects and places.

Digital Inclusion among Community Older Adults in the Republic of Korea: Measuring Digital Skills and Health Consequences.

Many older adults are increasingly embracing digital technology in the Republic of Korea. This study investigated the relationship between the digital skills of Korean older adults and their perceived health status and digital technology application for health promotion. This mixed-method study comprised a community survey of 434 older adults aged ≥65 in two cities in South Korea, followed by focus group interviews. Five types of digital skills, 'operational internet skills', 'information navigation skills', 'social skills', 'creative skills', and 'mobile skills', were measured using the LSE digital skill measurement instrument. Multivariable analysis identified the influence of digital skills on health-related outcomes. Among them, 'social skills' associated positively with self-rated health (β 0.37, 95%CI 0.08, 0.65). 'Information navigation skills' contributed positively to the use of digital technology and the internet for a healthy lifestyle in terms of improving eating habits (β 0.43, 95%CI 0.09, 0.77), accessing healthcare (β 0.53, 95%CI 0.21, 0.85), and accessing long-term care services (β 0.45, 95%CI 0.11, 0.79). Thematic analysis revealed that the study participants use Korean language-based resources such as Naver and Kakao Talk for social connection to promote a healthy lifestyle. This study concludes that encouraging initial and sustained use of the internet and enhancing digital skills among Korean older adults can promote active and healthy aging.

Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition

Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.