Efficient Navigation Research Articles

Healthcare system navigation difficulties among informal caregivers of older adults: a logistic regression analysis of social capital, caregiving support and utilization factors

BackgroundInformal caregivers of older adults play a vital role in improving the degree to which older adults access community and healthcare services in a seamless and timely manner. They are fulfilling important navigation and support roles for their older care recipients. However, there is still little knowledge of the most significant facilitators and barriers to effective and efficient system navigation among caregivers. This paper aims to fill these knowledge gaps through investigation of the key factors (i.e., social capital/cohesion, caregiving supports, and utilization factors) affecting navigation difficulties faced by informal caregivers of older adults.MethodsThe Behavioural-Ecological Framework of Healthcare Access and Navigation (BEAN) model is used to frame the study. Using the General Social Survey on Caregiving and Care Receiving 2018, we analyzed 2,733 informal caregivers whose primary care recipients were aged 65 or older. Hierarchical logistic regression was conducted to identify the relationship between system navigation difficulties among informal caregivers and four sequentially ordered blocks of predictors: (1) sociodemographic (2), social capital/cohesion (3), caregiving supports, and (4) healthcare demand.ResultsThe fully adjusted model showed that the probability of reporting navigation difficulties was lower for caregivers with social capital/cohesion compared to those without social capital/cohesion. In comparison, the probability of reporting navigation difficulties was higher among caregivers with caregiving support and among caregivers whose care receivers use a higher amount of health service use. Several sociodemographic covariates were also identified.ConclusionOur findings support certain aspects of the BEAN model. This study extends our understanding of potential facilitators and barriers that informal caregivers of older adults face while navigating complex community and health systems. There is a need to implement coordinated schemes and health policies especially for older adults with mental/neurological issues to address the challenges of their caregivers given the specific vulnerability identified in this study. The need for further research using different approaches to examine the disproportionate impact of COVID-19 on caregivers’ system navigation experience is crucial.

Ship model-based route optimisation for decision support in deep sea shipping

Abstract We present a new approach and route optimization methodology to support analysis and planning of vessel and fleet performance in deep sea shipping for green, energy efficient, and safe navigation. The developed methodology combines the ship technical characteristics based on a ship model developed for a particular vessel type and energy-saving technology options and an optimization algorithm taking into account weather conditions. Optimization involves two stages: graph construction and Dynamic Programming labelling algorithm implemented to solve the shortest path problem with variable speed. The new approach is implemented as part of a decision-support tool EcoRouter enabling the user to conduct analysis of safe and Pareto optimal solutions. Several applications to support real fleet planning and ship performance analysis have been identified including project engineering for future energy-saving ship technologies, for example, wind-assisted propulsion.

An Efficient Reinforcement Learning-Based Cooperative Navigation Algorithm for Multiple UAVs in Complex Environments

An Efficient Reinforcement Learning-Based Cooperative Navigation Algorithm for Multiple UAVs in Complex Environments

Development and implementation of an equitable and efficient navigation program to address supportive care needs in patients with cancer.

58 Background: Despite evidence backing the use of navigation along the cancer care trajectory, existing programs struggle to deliver services efficiently and equitably. To bridge this gap, we developed and implemented a patient-centric, data-driven navigation program that leverages a risk-stratification approach to prioritize and address unmet needs. Methods: Our program was developed with key principles to address inefficiencies in existing navigation systems. First, we embedded the program within our institution’s Epic environment, using data-driven risk calculators, dashboards, flowsheets, and templates to support the navigation curriculum. Second, the program structure aligned with the patient’s journey from initial consult through treatment, focusing on early identification of unmet needs, centralized resourcing, and remote symptom monitoring for high-risk patients. Third, we unified the workflows of navigators, intake personnel, triage nurses, and providers, fostering system-wide collaboration. Results: The program has been successfully implemented in patients with new consults in gastrointestinal, breast, and hematologic oncology. To date, 2,080 patients have had an initial assessment leading to a Navigation Assessment Score (NAS) that predicts longitudinal burden of unmet needs and directs subsequent resourcing. 226 referrals have been generated to: cancer rehabilitation (76), nutrition (60), psychosocial oncology (48), tobacco cessation (39), and palliative care (3). Remote symptom monitoring was piloted in 34 gastrointestinal oncology patients meeting the calculated highest decile risk for unplanned acute care event use. Proportions of patients with a NAS score (N=2,080): 69.3% White, 17.7% Black, 3% Latinx, 1.8% Asian, 0.9% American Indian; referrals (N=226): 61.2% White, 23.0% Black, 4.9% Latinx, 1.1% Asian, 0.5% American Indian; remote symptom monitoring (N=34): 79.4% White, 11.8% Black, 5.9% American Indian. Except for Latinx patients, these distributions align with the patient population in gastrointestinal, breast, and hematologic oncology (N=4,267; 68.0% White, 22.5% Black, 6.4% Latinx, 1.9% Asian, 0.9% American Indian). Conclusions: Development and implementation of our program is feasible, and the approach has potential to mitigate disparities in cancer service delivery and outcomes. Ongoing efforts include exploring accessibility among Latinx patients and developing the business case for sustainability and scaling.

Data-driven investigation of thickness variations in multilayer thin film coatings

Abstract Design and fabrication of multilayer thin film coatings for photonics applications require careful consideration of various parameters such as layer thickness, refractive indices and number of stacks. A growing trend uses machine learning for efficient navigation in the complex parameter space of photonics applications to efficiently extract valuable insights from the extensive datasets and to predict the optical performance. We developed an approach that combines Monte-Carlo and Finite-Difference Time-Domain simulations to model multilayer thin films. After conducting 95 200 runs, the data were analyzed using Neural Network fitting to explore how thickness variations influence the optical performance. An experiment validation on magnetron sputtered coated samples demonstrates the high accuracy of our method in predicting the optical performance of the thin film stacks (R 2 > 0.99), contributing to the understanding and enhancement of photonics stack properties for diverse optical applications using machine learning approaches.

Global path planning of unmanned ship island region based on improved A* algorithm

Abstract To solve the path planning problem for unmanned boats in island and reef areas, this paper introduces an adaptive heuristic function specifically for island and reef areas based on the traditional A* algorithm, in order to effectively handle the obstacle avoidance problem. A new cost function is designed to balance the relationship between path length and safety. The sea chart is converted into a path planning map by using the grid method, and the path planning is carried out. The paper compares the performance of the new path-planning algorithm with other path-planning algorithms through experiments, and the improved A* algorithm for global path planning of unmanned boats in island and reef areas shows excellent performance in various complex environments, effectively improving the island and reef area navigation efficiency.

Integrated terahertz topological valley-locked power divider with arbitrary power ratios

Integrated power dividers (PDs) are essential in terahertz (THz) communication and radar systems, but miniaturization often leads to performance degradation due to fabrication inaccuracies and sharp bends. Topological photonics offers a solution to these issues, yet creating THz power dividers with arbitrary splitting ratios remains challenging. We present a design methodology for on-chip topological THz power dividers with customizable splitting ratios using valley-locked photonic crystals. These crystals feature a tri-layered structure with two distinct valley Chern number layers and an intermediate semimetal layer. Utilizing the Jackiw–Rebbi model, we show that the characteristic impedance of the valley-locked photonic crystals, and thus the power division ratio, can be tuned by adjusting the semimetal layer width. Our approach is validated through simulations and experiments for both equal (1:1) and unequal (4:9) power ratios. This method enables efficient navigation around sharp bends and robust THz on-chip connectivity.

DEVELOPMENT OF RESPIRATORY SYSTEM RPG GAME USING UNITY WITH A* (A STAR) ALGORITHM

This research addresses the need for more engaging and interactive methods to improve elementary students' understanding of complex scientific concepts, particularly the respiratory system. To overcome the limitations of traditional teaching methods, an educational Role-Playing Game (RPG) incorporating the A* (A-Star) algorithm was developed for optimal game navigation. The study followed the ADDIE development model, which involves Analysis, Design, Development, Implementation, and Evaluation. During the analysis phase, learning needs were determined through interviews and classroom observations. The design phase involved creating game scenarios and integrating educational content with interactive elements. The A* algorithm was applied during development to ensure efficient navigation. The game was implemented in a 5th-grade classroom in Kuningan, and its effectiveness was evaluated using pre-tests, post-tests, and student questionnaires. Results demonstrated a significant increase in students' understanding, with average post-test scores rising from 58 to 85. Feedback from both students and teachers was very positive, with the game receiving a 94.2% acceptance rate. The study suggests that RPG-based educational games with intelligent algorithms like A* can greatly enhance science education by offering a more engaging and effective learning experience, contributing to advancements in technology-based learning and setting a standard for future educational game development.

Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model’s performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model’s efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Enhanced Particle Swarm Optimisation for Multi-Robot Path Planning with Bezier Curve Smoothing

This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics.

Replay shapes abstract cognitive maps for efficient social navigation

Replay shapes abstract cognitive maps for efficient social navigation

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.

Efficient navigation of cargo-towing microswimmer in non-uniform flow fields

The vision of deploying miniature vehicles within the human body for intricate tasks holds tremendous promise across engineering and medical domains. Herein, optimal navigation of a cargo-towing swimmer under an applied zig-zag flow is studied by employing direct numerical simulations coupled with a deep reinforcement learning algorithm. Tasks include navigation in flow and shear-gradient directions. We initially explore combinations of state inputs, finding that optimal navigation necessitates swimmers to perceive hydrodynamics and alignment, surpassing reliance solely on hydrodynamic signals while considering their memories. Next, we study combinations of action spaces, allowing dynamic changes in swimming and/or rotational velocities by tuning B1 and C1 parameters of the squirmer model, respectively. By keeping both parameters fixed, cargo-towing swimmers demonstrate superior performance in the flow direction compared to swimmers without load due to tumbling movements influenced by shear flow. In the shear-gradient direction, swimmers without load outperform cargo-towing swimmers, with performance decreasing as load length increases. Across the combination of allowing B1 and C1 to change, the policies from solely dynamic B1 actions demonstrate superior navigation. The policies are then used as a showcase against naive cargo-towing and inert colloidal chains. A t-distributed stochastic neighbor embedding analysis reveals the complex interplay between perceived hydrodynamic signals and swimmer position. In the flow direction, swimmers align effectively with regions of maximum velocity, while in the shear-gradient direction, periodic transitions from minimum to maximum state values occur. Comparing pullers, pushers, and neutral swimmers, cargo-towing swimmers show a reversal in swimming velocity trends, with pullers outpacing neutral and pusher swimmers, irrespective of load lengths. Published by the American Physical Society 2024

Fast and Efficient Drone Path Planning Using Riemannian Manifold in Indoor Environment

This paper introduces an innovative dual-path planning algorithm rooted in a topological three-dimensional Riemannian manifold (T3DRM) to optimize drone navigation in complex environments. It seamlessly integrates strategies for both discrete and continuous obstacles, employing spherical navigation for the former and hyperbolic paths for the latter. Serving as a transformative tool, the T3DRM facilitates efficient path planning by transitioning between discrete and continuous domains. In uncertain environments with unpredictable obstacle positions, our methodology categorizes these positions as discrete or continuous based on their distribution patterns. Discrete obstacles exhibit random distributions, while continuous obstacles display symmetrical patterns with continuity. Leveraging topological metrics, the T3DRM efficiently classifies these patterns for effective path planning. The findings of this research demonstrate the efficiency of path planning based on classified obstacle positions, enabling swift and efficient drone navigation. This research introduces a pioneering application of a T3DRM, accelerating drone navigation in uncertain environments through a dual approach that simultaneously transforms navigation in primal and dual domains. By enabling spherical and hyperbolic navigation concurrently, the T3DRM offers a comprehensive solution to discrete and continuous path planning challenges. The proposed approach can be used for various indoor applications, especially for warehouse management, surveillance and security, navigation in complex structures, indoor farming, site inspection, healthcare facilities, etc.

Unstructured environment navigation and trajectory calibration technology based on multi-data fusion

In order to effectively improve the accuracy and efficiency of unstructured environment navigation and trajectory calibration, a multi data fusion based unstructured environment navigation and trajectory calibration method was studied. Described the basic principles of the Strapdown Inertial Navigation System and Doppler Log. After introducing DVL velocity information and relative position information of underwater robots into SINS, the Kalman filter fusion algorithm was used to obtain the system's state equation and measurement equation. The navigation parameter error and device error parameters of the system were selected as the state variables. The difference between SINS output speed and DVL measurement speed, as well as the difference between SINS output position and relative position, are used as the speed and position measurements of the measurement system. Error estimation is obtained through indirect filtering, and the accuracy of navigation and positioning is improved through output correction. The experimental results show that the proposed method has a good calibration effect and can effectively improve the accuracy and efficiency of trajectory calibration in unstructured environments.

Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study

Robot semantic navigation has received significant attention recently, as it aims to achieve reliable mapping and navigation accuracy. Object detection tasks are vital in this endeavor, as a mobile robot needs to detect and recognize the objects in the area of interest to build an effective semantic map. To achieve this goal, this paper classifies and discusses recently developed object detection approaches and then presents the available vision datasets that can be employed in robot semantic navigation applications. In addition, this paper discusses several experimental studies that have validated the efficiency of object detection algorithms, including Faster R-CNN, YOLO v5, and YOLO v8. These studies also utilized a vision dataset to design and develop efficient robot semantic navigation systems, which is also discussed. According to several experiments conducted in a Fablab area, the YOLO v8 object classification model achieved the best results in terms of classification accuracy and processing speed.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract