Navigation Components Research Articles

WC-CP: A Bluetooth Low Energy Indoor Positioning Method Based on the Weighted Centroid of the Convex Polygon

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily deployable, flexible, and efficient. This paper focuses on indoor positioning solely based on BLE. Motivated by two observations, namely, that (i) involving more anchor nodes can enhance positioning accuracy, and that (ii) narrowing the area for unknown location determination can also lead to improved accuracy, a new distance-based method, the Weighted Centroid of the Convex Polygon (WC-CP), is proposed. While it is generally acknowledged that incorporating more anchor nodes can enhance indoor positioning performance, the current state of the art lacks a robust methodology for selecting and utilizing these nodes. The WC-CP approach addresses this gap by introducing a systematic and efficient method for identifying and employing the most suitable anchor nodes. By avoiding nodes that could potentially introduce significant errors or lead to incorrect localization, our method ensures more accurate and reliable indoor positioning. The efficacy of WC-CP is demonstrated in an indoor environment, achieving an RMSE of 1.35 m. This result shows significant improvements over three state-of-the-art approaches, about 34.15% better than LSBM, 32.50% better than TWCBM, and 30.05% better than ITWCBM. These findings underscore the potential of WC-CP for enhanced accuracy and reliability in indoor positioning based on BLE.

Monocular Vision Sensor-Based Indoor Road and Stair Detection

Road detection is an essential component of indoor robot navigation. Vision sensors have great advantages in road detection as they can provide rich information in terms of environmental perception. In this paper, a monocular vision sensor-based method for indoor road and stair detection is proposed, which detects feasible areas in indoor environments very fast without paying attention to detailed features of walls or other obstacles. More specifically, for a given indoor road image captured by an on-board vision sensor, the simple linear iterative clustering (SLIC) algorithm-based approach for efficient image segmentation is introduced. Then, according to the DBSCAN algorithm, the generated superpixels are clustered to form large areas of view. The initial road area is obtained through a safe window on the middle bottom of the image. In order to achieve a more accurate road segmentation, the initial image is processed by the binary search, edge detection based on the Canny operator and straight-line detection and location based on the Hough transform, which integrates edge and stair information into road detection. Several experiments are performed to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method could accurately detect road information and staircase information in images and succeeds in addressing the indoor road-detection problem.

A Comparison Between A* and RRT Algorithm in Path Planning for Mobile Robot

The ability of mobile devices to navigate environments is vital. Navigation plays a critical role in averting collisions and hazardous situations.The design of paths is an essential component of robot navigation. comprising the ability to determine the optimal route from the robot's current location to a given destination. And in all state-of-the-art algorithms for robots to design the optimal route, A* and RRT algorithm stand out as the two most widely used path planning techniques, one is graph-based approach while the other is sample-based. In this paper, some variants or improvement of the two methods will be described. And a comparison about A* and RRT will be presented with appropriate criteria, which are path length and computational time. The main finding is that the A* method outperforms the Rapidly Exploring Random Tree (RRT) approach in terms of computational efficiency and path distance optimization. But it requires to consider more thoughts in applications. The comparison of the A* and RRT algorithms helps comprehend their applicability in various application circumstances. In this work, Evaluating these aspects altogether comprehensively provides a deeper understanding of the suitability of each algorithm for specific robotic navigation tasks.

(Don't) look where you are going: Evidence for a travel direction signal in humans that is independent of head direction.

We often assume that travel direction is redundant with head direction, but from first principles, these two factors provide differing spatial information. Although head direction has been found to be a fundamental component of human navigation, it is unclear how self-motion signals for travel direction contribute to forming a travel trajectory. Employing a novel motion adaptation paradigm from visual neuroscience designed to preclude a contribution of head direction, we found high-level aftereffects of perceived travel direction, indicating that travel direction is a fundamental component of human navigation. Interestingly, we discovered a higher frequency of reporting perceived travel toward the adapted direction compared to a no-adapt control-an aftereffect that runs contrary to low-level motion aftereffects. This travel aftereffect was maintained after controlling for possible response biases and approaching effects, and it scaled with adaptation duration. These findings demonstrate the first evidence of how a pure travel direction signal might be represented in humans, independent of head direction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Autonomous traffic sign detection for self-driving car system using convolutional neural network algorithm

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data.

Non-Cooperative LEO Satellite Orbit Determination Using Single Station for Space-Based Opportunistic Positioning

Space-based opportunistic positioning is a crucial component of resilient positioning, navigation, and timing (PNT) systems, and it requires the acquisition of orbit information for non-cooperative low Earth orbit (LEO) satellites. Traditional methods for orbit determination (OD) of non-cooperative LEO satellites have difficulty in achieving a balance between reliability, hardware costs, and availability duration. To address these challenges, this study proposes a framework for single-station orbit determination of non-cooperative LEO satellites. By utilizing signals of opportunity (SOPs) captured by a single ground station, the system performs initial orbit determination (IOD), precise orbit determination (POD), and orbit prediction (OP), enabling the long-term determination of satellite positions and velocities. Under the proposed framework, the reliability and real-time performance are dependent on the initial orbit determination and the orbit calculation based on the dynamical model. To achieve initial orbit determination, a three-step algorithm is designed. (1) An improved search method is employed to estimate a coarse orbit using single-pass Doppler measurements. (2) Data association is conducted to obtain multi-pass Doppler observations. (3) The least squares (LS) is implemented to determine the initial orbit using the associated multi-pass Doppler measurements and the coarse orbit. Additionally, to enhance computational efficiency, two fast orbit calculation algorithms are devised. These algorithms leverage the numerical stability of the Runge–Kutta integrator to reduce computations and exploit the strong correlation among nearby time intervals of orbits with small eccentricities to minimize redundant calculations, thereby achieving orbit calculation efficiently. Finally, through positioning experiments, the determined orbits are demonstrated to have accuracy comparable to that of two-line elements (TLE) updated by the North American Aerospace Defense Command (NORAD).

IS-CAT: Intensity-Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition.

LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.

An Eye-tracking Study: Investigating the Impact of Website Theme Colors on Viewer Engagement

The Internet’s rapid evolution has transformed the media landscape, prompting brands and companies to transition from traditional print and TV to online media. Enhancing website design has become a focal point for effectively conveying information and capturing users’ attention. Among the various elements involved, the choice of web page theme color stands out as a crucial visual component in web navigation, significantly impacting the viewer’s user experience. Investigating the influence of web page colors on viewer attention is imperative. This study employs eye-tracking technology to delve into the impact of diverse web page colors on participants’ attention, particularly on news websites. An eye tracker was employed to meticulously record participants’ gaze patterns while browsing by experimenting with new pages featuring distinct theme colors. Data analysis revealed that red and blue colors garnered the most attention from viewers, followed by yellow, while gray-themed sites performed the least effectively in this regard. These findings offer valuable insights for web design improvement. In the context of news websites, red and blue are particularly effective in capturing and guiding people’s attention. When emphasizing or conveying breaking news, incorporating blue or red icons or relevant elements can be a powerful means of communication; however, it is equally important to be mindful of the potential for viewer visual fatigue when using highly saturated colors over a significant area. Therefore, designers must exercise care in determining the color balance when employing high-contrast and high-saturation hues.

Adapting a Remotely Delivered Patient Navigation Program for Colorectal Cancer Screening in Primary Care: Important Considerations for Rural Contexts.

Colonoscopy is a critical component of colorectal cancer (CRC) screening and patient navigation (PN) improves colonoscopy completion. A lay navigator remotely providing navigation across rural primary care organizations (PCOs) could increase PN access. In preparation for the Colonoscopy Outreach for Rural Communities (CORC) study, we examined partners' perspectives on contextual factors that could influence CORC program implementation, and adaptations to mitigate potential barriers. We interviewed 29 individuals from 6 partner PCOs and the community-based organization (CBO) delivering the PN program. An analysis approach informed by Miles, Huberman, and Saldana identified critical themes. Results are reported using the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME). Potential barriers included that rural patients are hard to reach remotely and might mistrust the navigator, and the CBO is unfamiliar with the patient communities and does not have patient care experience or pre-existing communication pathways with the PCOs. Program content and navigator training was adapted to mitigate these challenges. Our study highlights contextual factors to account for before implementing a remote, centralized patient navigation program serving rural communities. Gathering partner perspectives led to intervention adaptations intended to address potential barriers while leaving the core components of the evidence-based intervention intact.

Stakeholder perspectives on six identified interpersonal communication components of patient navigation in breast cancer care.

Although identified as a key competency domain and a needed area of professional development, interpersonal communication in breast cancer care patient navigation is understudied. Moreover, the patient-navigator relationship may be influenced by the interpersonal communication skills and behaviors of the patient navigator. This paper reports on the interpretation step of a concept mapping study, where key stakeholders shared their perspectives on six identified interpersonal communication components of breast cancer care patient navigation. This study utilized concept mapping, a community-engaged mixed method approach. After conducting brainstorming, sorting, and concept mapping analysis, a six-cluster concept map of interpersonal communication in breast cancer care patient navigation was identified. Interpretation sessions with each participant group (patients, patient navigators, administrators) allowed both naming and more in-depth exploration of the six clusters. The sessions were led by a facilitator, the PI, and were audio recorded and transcribed. Six 2-h interpretation sessions were conducted with 21 participants, including patients with breast cancer, breast cancer patient navigators (lay or medically trained), and patient navigation administrators from Western Pennsylvania. Through a group consensus process, the six clusters were named. Participants identified that all six identified components were essential to patient navigation, but the ability to build patient-centered trust and relationships and maintain professional communication were the most impactful components of the patient-navigator relationship. These findings validate the importance of interpersonal skills and behaviors of patient navigators in breast cancer care. These findings can inform the patient navigation role description, competencies, and the development of curriculum for training and metrics for evaluation.

Robotic-Assisted Single-Position Prone Lateral Lumbar Interbody Fusion: Indications, Techniques, and Outcomes.

Robotic-Assisted Single-Position Prone Lateral Lumbar Interbody Fusion: Indications, Techniques, and Outcomes.

Innovative meanings of upbringing: game as a navigator of personality development

Introduction. Modern conditions pose the task of modernizing education in general and searching for innovative meanings of upbringing in particular. The relevance of this issue is associated with the need to rethink the innovative meanings of upbringing and to identify qualities, structural elements, and associations, which were missing before. The aim of the study is to develop an innovative classification of games (necessary as a social navigator of personality development), representing a special innovative meaning of upbringing. Materials and methods. The study involved 86 practicing teachers and students of the Institute of Psychology and Pedagogy of I.A. Bunin Yelets State University (Russian Federation), students of pedagogical classes. All respondents had different work experience. The questionnaire contained 13 questions (9 – with a choice of answers and 4 – with a free interpretation of the answer). For statistical data processing, the methods of percentage distribution were used. Results. The empirical results showed that 63% of respondents had a positive attitude toward games and gaming activity, 67% preferred team games, 35% liked the game process itself, and 30% of respondents indicated the prospect of personality development as a motivation for the game. The study also demonstrated that despite the choice of six classes of games, the respondents gave priority to computer games (19%), board games (28%), action-oriented games (27%), and phygital games (16%). This state of affairs made it possible to develop an innovative classification of games as a navigator of personality development. Conclusion. The game as a social navigator of personality development and the author’s classification of games set innovative meanings of upbringing, related to the navigational component of personality development, specific classification of games, development of the game model, and improvement of the game technology/algorithm. The developed author’s classification of games has no exact counterparts in the practice of upbringing. Besides, the proposed author’s classification of games can be considered in terms of improving the methodological support for the modern upbringing process.

A Community–Academic Partnership to Explore and Address Cancer Disparities in Southwest Chicago Arab Americans

Abstract: Background: Despite the need to consider multiple sources of evidence to guide locally and culturally relevant interventions, few studies have documented the process by which evidence is integrated. Objectives: We leveraged a community–academic partnership to describe a participatory approach to integrating community and academic sources of evidence to inform cancer programming priorities in the Arab American (ArA) community in Southwest Chicago. Methods: Informed by Intervention Mapping, this study comprised three phases led by community and academic partners: 1) qualitative assessment of cancer-related priorities through eight focus groups with 48 ArA community members, 2) a focused literature review to identify models of cancer interventions implemented with ArAs, and 3) integration of focus group and literature review findings and development of a strategy for a community-based cancer program administered by the community partner. Results: Focus groups revealed attitudes and beliefs across the cancer control continuum. The literature review highlighted two cancer interventions utilizing education, community health workers, and patient navigation components. Through facilitated discussions with community partners, we integrated community and academic sources of evidence to develop a comprehensive cancer program plan that is informed by the data we generated as well as our community partners' preferences and organizational capacity. Conclusions: Our participatory approach for integrating community and academic sources of evidence generated a locally relevant strategy to address cancer burden in the ArA community in Chicago. We discuss the benefits and challenges of utilizing this approach in intervention development.

A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

Intelligent Blind-User Navigational Device Utilizing Image Processing

There are many thousands of blind people in the world who frequently require support. Since the white cane has long been known as an essential component of blind people's navigation, efforts have been made to enhance it by adding new sensors. Blind people are relatively comfortable inside their homes because they can pinpoint where things are, but in the outside world, as the objects are in motion, they face difficulties in locating the objects without an assistance. The research put forth strives to develop a sound navigation and range system for blind people. A real-time obstacle identification has been accomplished using ultrasonic sensors. In order to create a safe path for navigation to a destination, image processing in real-time is used for providing the blind person's perception of the size and distance of the obstacle utilizing raspberry pi for object detection. The suggested method makes use of a camera for recording the video sequence of surroundings, which it subsequently processes using a vocal input from a person who is visually impaired. It takes the location data while taking the (GPS) module for outdoor use. The location detail gathered is transported to the cloud.

Prediction of Array Antenna Assembly Accuracy Based on Auto-Encoder and Boosting-OSKELM

As a critical component for space exploration, navigation, and national defense, array antenna secures an indispensable position in national strategic significance. However, various parts and complex assembly processes make the array antenna hard to meet the assembly standard, which causes repeated rework and delay. To realize the accurate and efficient prediction of the assembly accuracy of array antenna, a prediction method based on an auto-encoder and online sequential kernel extreme learning machine with boosting (Boosting-OSKELM) is proposed in this paper. The method is mainly divided into two steps: Firstly, the auto-encoder with the fine-tuning trick is used for training and representation reduction of the data. Then, the data are taken as the input of Boosting-OSKELM to complete the initial training of the model. When new sample data is generated, Boosting-OSKELM can realize the online correction of the model through rapid iteration. Finally, the test shows that the average MSE of Boosting-OSKELM and ANN is 0.061 and 0.12, and the time consumption is 0.85 s and 15 s, respectively. It means that this method has strong robustness in prediction accuracy and online learning ability, which is conducive to the development of array antenna assembly.

HIV Treatment and Mental Health Outcomes Among Gay, Bisexual, and Other Men Who Have Sex With Men Living With HIV in a Pilot Multicomponent Intervention in Guatemala City.

Globally, gay, bisexual, and other men who have sex with men (GBMSM) are disproportionately affected by HIV, but few interventions address mental health and HIV outcomes among GBMSM living with HIV. The purpose of this study was to pilot a multicomponent intervention, integrating emotional well-being (EW) and health navigation for GBMSM living with HIV in Guatemala City. We implemented a 12-month intervention, including a four-session EW component and a health navigation component among newly diagnosed and reengaged GBMSM with HIV (n = 112). We conducted socio-behavioral surveys at baseline and endline to measure patient characteristics and HIV, and mental health outcomes as well as viral load testing. We documented participation in EW sessions and used a smartphone application to track navigation mode, frequency, and content. Using first-difference estimation modeling, we assessed associations between participation in EW and navigation and mental health (depression and anxiety symptoms) and HIV outcomes (HIV treatment adherence, viral suppression). Acceptability was high for EW (86%) and navigation (99%). During the intervention, viral suppression increased significantly and anxiety and depression decreased significantly. Participation in EW sessions and greater navigation frequency and duration were associated with being suppressed, whereas higher emotional navigator support was associated with being unsuppressed. Participation in EW sessions was associated with reduced anxiety. Findings suggest that multicomponent interventions integrating individual counseling and navigation may promote EW and sustained viral suppression. Future intervention research is needed to confirm whether HIV and mental health outcomes are attributable to the intervention and to assess mechanisms of influence.

Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance.

Hippocampal spatial view cells, place cells, and concept cells: View representations.

A commentary is provided on issues raised in the Special Issue of Hippocampus (2023) on hippocampal system view representations. First, the evidence for hippocampal and parahippocampal spatial view cells in primates including humans shows that the allocentric representations provided by at least some of these cells are very useful for human memory in that where objects and rewards are seen in the world "out there" is a key component of episodic memory and navigation. Spatial view cell representations provide for memory and navigation to be independent of the place where the individual is currently located and of the egocentric coordinates of the viewed location and the facing direction of the individual. Second, memory and navigation in humans are normally related to the visual cues encoded by spatial view cells that define a location "out there" such as a building, hill, and so forth, not to an unmarked place without local cues and identified only by distant environmental/room cues. Third, "mixed" representations, for example of particular combinations of spatial view and place, can arise if the training has been for only some combinations of place and view, for that is what can then be learned by the hippocampus. Fourth, rodents, with their much less good visual acuity (~1 cycle/° in rats, compared with ~60 cycles/° for the human fovea), and rodents' very wide viewing angle for the world (~270°) might be expected, when using the same computational mechanisms as in primates, to use widely spaced environmental cues to define a place where the rodent is located, supported by inputs about place using local olfactory and tactile cues. Fifth, it is shown how view-point dependent allocentric representations could form a view-point independent allocentric representation for memory and navigation. Sixth, concept cells in humans and primates with connectivity to the hippocampus are compared.

Integrity Management of the Reachable Space With Lane Grid Maps

For an autonomous vehicle, reliable situation understanding is a key component of safe navigation. An incorrect prediction of an upcoming situation means that erroneous information may be supplied to the decision-making process, leading to hazardous outcomes. It is therefore of great importance to estimate the driving areas that are reachable by other interacting road users, without introducing misleading information. This paper presents a means of handling the integrity of prediction information, given the imperfection of object prediction, via a Lane Grid Map, that is to say a spatial representation of the situation at a tactical level, based on the topological layer of a high-definition map. We demonstrate experimentally, using real data, how the spatial sampling step of the grid representation can be used to manage the integrity of prediction information. Moreover, addressing interactions during the prediction makes it possible to handle some particular situations safely. We show how some interactions can be utilized via the concept of neutralization. To quantify the integrity of the prediction, we propose the use of two metrics, namely False Negative Rate and Neutralized Time Interval. Experiments were carried out with three vehicles to evaluate the integrity of the prediction using these metrics.