Navigation Issues Research Articles

Design and development of an eHealth intervention to support self-management in people with musculoskeletal disorders: 'eHealth: it's TIME'.

This study aimed to co-design and develop a user-centred, theory-based eHealth-mediated self-management support follow-up prototype for adults with musculoskeletal disorders. A three-step system development cycle was employed. Step 1 involved creating intervention features and content, with two focus groups reviewing prioritised eHealth intervention elements based on earlier research. Step 2 involved heuristic testing using Nielsen's 10 heuristic principles. Step 3 incorporated qualitative think-aloud interviews and the System Usability Scale. Republic of Ireland. Step 1 included adults with musculoskeletal disorders (n = 12). Step 2 involved five reviewers. Step 3 included people with musculoskeletal disorders (n = 5) and musculoskeletal physiotherapists (n = 5). Participants in step 1 approved four main intervention components, which map to recognised theoretical frameworks, and suggested increased use of visual and interactive elements. Heuristic testing in step 2 identified design and navigation issues. In Step 3, usability testing, additional navigation, content and design recommendations were identified. The overall median system usability score (interquartile range) was 75 (0) out of 100 for adults with musculoskeletal disorders and 77.5 (2.5) out of 100 for musculoskeletal physiotherapists, indicating good usability. A theory-based, user-centred eHealth-mediated follow-up self-management support prototype has been developed for people with musculoskeletal disorders, with the next steps focusing on feasibility testing in clinical practice, with a more diverse population.

Measuring Tilt with an IMU Using the Taylor Algorithm

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning.

Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.

Algorithms for Route Planning and Navigation of Unmanned Aerial Vehicles

A brief overview of the results of recent research published in open sources in the field of route planning and navigation algorithms for unmanned aerial vehicles (UAV) is presented. Works devoted to global and local planning of trajectories taking into account known and detected obstacles in flight, as well as issues of navigation of groups of drones, are considered. Various approaches are analyzed, including graph algorithms (A*, Dijkstra, Rapidly-exploring Random Trees), methods of data mining in real time, potential fields. Special attention is paid to work on the use of neural networks and machine learning, SLAM and multi-agent technologies for planning UAV routes. The advantages and disadvantages of the main groups of algorithms are considered. A conclusion is drawn about the prospects for using hybrid methods, as well as machine learning technologies, to build intelligent UAV traffic control systems.

Autonomous navigation decision-making for ships in complex estuarine waters: Methodology and validation

This study focused on key scientific issues in the autonomous navigation for ships, including constraints on environmental and navigation rules, limitations on ship maneuverability, and collision avoidance mechanisms, to address the challenges of autonomous navigation decision-making for ships in complex estuarine waters and validate the feasibility and accuracy of the proposed methodology. A novel autonomous navigation decision-making method that can dynamically adapt to residual errors in a system and the random movements of target ships was proposed. The predictive model used to calculate decision plans and the simulation model used to execute and validate decision plans were separated. Environmental constituent elements were classified and modeled to digitize the environment. A navigation scenario was constructed based on the fusion of virtual and real information. The control and process prediction methods were proposed based on the nonlinear ship maneuvering characteristics. Navigation rules were summarized, analyzed, and integrated into the decision-making and execution process. The collision avoidance mechanism and calculation method for feasible course and speed range were studied. Simulation experiments were conducted using the North Channel of the Yangtze River Estuary as an example. The results demonstrated that the proposed method can accurately make autonomous navigation decisions, safely avoid collisions, and timely track routes in nearly realistic scenarios.

Assessing public satisfaction of public service application using supervised machine learning

The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.

Safety of navigation along the Northern Sea Route: Anna Ananeva’s solution

An expert in the field of oceanology and cartography tells about a unique methodology that can significantly advance the development of the strategic transport corridor. According to Rosatom forecasts, as early as 2030, cargo turnover along the Northern Sea Route could reach 193 million tons, and in 2035 — 220 million tons. To solve these tasks, it is essential to find solutions to the issues of safe and efficient navigation and increase the permeability of the key transport route. One of the promising proposals for the development of the global transport corridor was created by Anna Ananeva, an expert in the field of oceanology and cartography and a reviewer of the international multidisciplinary journal “Current Research”. Her development increases the safety of cargo transportation and can save significant funds for transport companies. The study that is important for the development of the country is presented in the material, which is of interest in the framework of training in the areas of “Navigation”, “Water Transport Management and Hydrographic Navigation Support”, and “Waterways, Ports, and Hydro-Technical Utilities” at higher educational institutions.

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

Development of a Prototype Global Positioning System Based Stick for Blind Patients

This paper presents a novel vision impairment assistive device to improve mobility and independence. This device consists of Arduino Nano microcontroller technology that powers the Satellite/GPS-based stick, which tracks and navigates in real-time. Arduino Nano’s adaptability and compactness enable our portable, affordable white cane replacement. Satellite signals let the stick locate the user, compute the best routes, and provide aural navigation cues through speakers or headphones. The obstacle detection sensors notify users of adjacent risks, improving safety. The proposed device is a stable and user-friendly technology that delivers a potential answer to visually impaired navigation issues after rigorous development and testing.

The impact of socioeconomic inequality on access to health care for patients with advanced cancer: A qualitative study

ObjectiveIn Canada, populations experiencing socioeconomic inequality have lower rates of access to screening and diagnosis and higher mortality rates than people from higher-income areas. Limited evidence exists concerning their experiences when living with advanced cancer. We explored how socioeconomic inequality shapes the experiences of patients with advanced cancer. MethodsWe utilized a qualitative study design that combined tenets of hermeneutic phenomenological inquiry and critical theory. Four individuals with advanced cancer from low-income neighborhoods, three family members, and six cancer care providers were accrued through a tertiary cancer center in a western Canadian city. One-on-one interviews and brief notes were used for data collection. Data were analyzed through thematic analysis. ResultsThree interrelated themes were identified: ‘Lack of access to socioeconomic supports,’ ‘Gaps in access to health care resources and services,’ and ‘Limited access to symptom relief.’ Patients experienced inadequate finances, housing, and transportation. Most patients lived alone and had limited family and social support. Patients reported lack of knowledge of available resources and health system navigation issues, including communication problems with providers and among levels of care. Cancer care providers and patients described issues achieving symptom relief as well as challenges associated with extensive disease. ConclusionsStudy findings suggest that socioeconomic inequality interferes with the ability of persons with advanced cancer to access health care and contributes to less optimal cancer outcomes. Socioeconomic inequality may increase symptom severity. Findings call for the development of tailored interventions for populations with advanced cancer and socioeconomic inequality.

Patient-Reported Wait Times and the Impact of Living with Chronic Pain on their Quality of Life: A Waiting Room Survey in Chronic Pain Clinics in Ontario, Manitoba, and Quebec

ABSTRACT Background Wait times at Canadian multidisciplinary pain clinics have been reported as excessive for nearly 2 decades. Aims The aim of this study was to gain insight into the patient experience of waiting for chronic pain specialty care. Methods A cross-sectional survey of new patients waiting for an appointment was conducted in six multidisciplinary pain clinics, including one pediatric clinic, in Ontario, Quebec, and Manitoba between February 2020 and October 2022. Participants were asked about the length of time they waited for their appointment since being referred, their quality of life, health care professionals seen while waiting, and an open-ended question, “Is there anything else you’d like to tell us?” Results Among the 493 adult and 100 pediatric respondents, 53% of adults and 82% of children reported wait times under 6 months, whereas 22% of adults and 4% of children waited longer than a year. Between 52% and 63% of adults and 29% to 48% of children reported being affected by chronic pain “quite a bit” or “extremely” on measures of quality of life. The most visited health care professionals while waiting for a pain clinic appointment were family doctors/nurse practitioners for adults and physiotherapists for children. Qualitative analysis of open-ended question responses revealed eight themes: system navigation issues, administrative issues, decreased quality of life, distress, self-advocacy, coping strategies, communication, and distrust. Conclusions Our findings provide real-time regional snapshots into the impact of long wait times experienced by Canadians living with chronic pain. There is an urgent need to better support patients during the waiting period. Expanding technologies such as electronic consultation hold great promise.

Older Adults' Collaborative Learning Dynamics When Exploring Feature-Rich Software

Collaborative learning has been suggested as a promising approach to help older adults learn new technology, however, its effectiveness has been understudied in the context of feature-rich applications. We conducted an observational study with the aim of identifying aspects of collaborative learning, including characteristics of collaborative partners, that impact older adults' exploratory learning behaviour in feature-rich software. We recruited 22 participants (6 younger adults and 16 older adults) who formed 5 same-age and 6 mixed-age dyads. These dyads worked together remotely to explore a feature-rich application, which was new to them. We classified dyadic interactions into four different collaboration dynamics characterized by distinct attributes. We discovered that effective communication and the ability to navigate the software independently enabled a successful collaboration dynamic that empowered learners. We showed that trust between partners enabled effective communication and we observed that the existing relationship between partners strongly impacted their communication patterns. The more complicated study tasks required participants to validate the correctness of their work and this validation was particularly difficult for some novice older adults who did not benefit from transfer learning and struggled with navigation issues.

Stereo vision based object detection for autonomous navigation in space environments

Obstacle detection and avoidance are the major issues in autonomous navigation for partially known or unknown environments. With the proliferation of space debris, researchers are actively investigating debris removal to facilitate future space operations. This calls for the development of autonomous navigation techniques for space missions. Free-space object detection is a crucial task in intelligent systems, particularly for path planning. In this study, we propose a stereo vision-based intelligent system for space object detection, using two vertically aligned omnidirectional stereo cameras separated by 10 cm. Firstly, a single-shot multibox detector (SSD) based on deep learning is employed to identify the objects present in the image. Then, the triangulation method is used to determine the distance between the object and the system. The proposed system can provide object depth information up to a maximum range of 1.1 km in a space environment.

On the activities of the Odesa branch of the Ukrainian Submarine Navigation School

Relevance. The issue of Ukrainian submarine navigation has received undeservedly little attention in the modern research literature. Despite the objective importance and the fundamentally innovative potential of this topic, little constructive investigation has been carried out so far in this area.Purpose. This paper aims to conduct a detailed prosographic analysis of the Odesa branch of the Ukrainian School of Submarine Navigation, which operated between 1877 and 1905. The study focuses on understanding the internal dynamics and the broader impact of this branch on national and international submarine navigation practices.Methodology. The study employed the methods of comparative analysis, hindsight, induction, and deduction, using both online and archival sources to cover as much information on the topic as possible. Cliometric studies were also conducted.Results. The presented study provides a prosographic analysis of the Odesa branch of the Ukrainian School of Submarine Navigation, which operated from 1877 to 1905. The branch�s basic structure has been defined. Various types of connections have been identified. The influence of the representatives of the Odesa branch on the representatives of other branches and national schools of submarine navigation that functioned in the Russian Empire at the beginning of the 20th century is illustrated.Conclusions. The findings will provide a solid basis for further scientific efforts in this area and for making strategic decisions to protect Ukraine's maritime interests. This investigation can also be used as a source of educational literature for historical and educational materials.

Digital Storytelling Intervention for Enhancing the Social Participation of People With Mild Cognitive Impairment: Co-Design and Usability Study.

Community-based social participation has shown promise in delaying cognitive decline in older adults with mild cognitive impairment (MCI) who are at risk of developing dementia. Although group storytelling interventions have proven effective, the need for a skilled workforce to support people with MCI can limit broader community implementation. Technology-based interventions may offer a solution to this limitation by replicating the abilities of therapists. This study aims to co-design a digital storytelling intervention and evaluate its usability. This co-design process involved 3 stages, engaging people with MCI (n=12), their caregivers (n=4), and therapists (n=5) in Beijing, China. In the first stage, we used card sorting and voting methods to identify potential incentives for social participation and target the specific abilities that people with MCI wanted to enhance. In the second stage, we conducted brainstorming sessions with people with MCI and their caregivers to identify the potential features of a digital storytelling application named Huiyou ("meeting new friends" in Chinese). Finally, we assessed Huiyou's usability with people with MCI and therapists, leading to iterative improvements based on the usability findings. We uncovered a crucial link between boosting the self-confidence of people with MCI and their ability to address social participation challenges. Notably, we identified memory improvement and enhanced language expression as key factors for effective communication with grandchildren. Subsequently, participants suggested features and interfaces to address these challenges, leading to the development of Huiyou, a group-based digital storytelling application featuring functions such as generating story materials, conducting memory retrieval activities, and sharing stories. It received an "excellent" rating in the User Experience Questionnaire benchmark, displaying high levels of attractiveness, dependability, stimulation, and novelty. People with MCI achieved an average task completion rate of 87% (n=19; SD 0.13) of the 22 tasks. However, feedback from people with MCI and therapists highlighted usability issues in navigation, activity management, user interface, and feature optimization, indicating a need for improved accessibility and efficiency. The co-design approach contributed to developing the Huiyou prototype, supporting community-based social participation. User feedback highlighted the potential of Huiyou to enhance well-being and facilitate meaningful social interactions while maintaining crucial existing relationships.

Interannual variability of ice cover across Davis Strait and the Labrador Sea for the period from 1979 to 2023

In conditions of rapid development of trade and transport communications, the issue of navigation through ice regime zones became of greater importance. In particular, reduction of the area of ice cover in the Canadian Arctic because of global warming opens up favourable prospects for further development of maritime shipping in this zone. Considering that the routes through the Northwest Passage are the shortest ones of all the routes connecting the Atlantic and Pacific oceans, it is clear that all aspects of this topic are of great importance and relevance. This especially applies to the aspects dedicated to studying the characteristics of appearance, formation, concentration, physical characteristics, trajectory of ice movement and forecasting the area of the ice cover in a given zone, i.e. the characteristics affecting the conditions of ice navigation and serving as determining factors in terms of navigation safety.
 The research examines some of these issues, namely the spread of the ice cover during the period of its maximum formation across Davis Strait and the Labrador Sea (where the routes through the Northwest Passage start) and the establishment of statistical relations between the ice spread and the temperatures of surface water and surface air. The research method to be used: synoptic-climatic and statistical analysis of numerical series of ice cover deviations from its average limit as of April 15 over the period of 1979-2023 within the zone in study.
 Based on the set goal, the study processed raw data on distribution of ice cover across Davis Strait and the Labrador Sea for the period of 1979-2023 and completed its statistical analysis. The results of the analysis showed presence of a statistically significant negative trend and cyclical fluctuations with periods of 3-6, 9, 10 and 13 years in relation to the interannual variability of ice cover distribution for the studied period. Positive trends are also observed in the interannual variability of surface water temperature and surface air temperature. After bringing the original series to a quasi-stationary form, a correlation analysis of the relationships between the ice cover distribution and the temperature of water and air was carried out. The results of the analysis determined zones and points with statistically significant correlation coefficients between them during the year. It turned out that the highest values of coefficients are observed from December to March, and the maximum one occurs in March.
 Systematization and analysis of the ice field across Davis Strait and the Labrador Sea during the period of maximum development made it possible to establish that the spread of ice between 60° and 55° north parallels was the most stable during the studied period, and the least stable was observed in the area below 55° north parallel.
 The results of the conducted research open up prospects for development of methods for forecasting the distribution of ice cover across Davis Strait and the Labrador Sea. This will make ice navigation in this area safer.

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios.

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that-according to the application and the robot's characteristics-it is recommended to use each of them, based on different conditions.

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency.

ALGORİTHMS FOR FİNDİNG NON-İNTERSECTİNG ROADS ON İMAGES

Research relevance The use of GPS for UAVs to determine their current coordinates (navigation) leads to a number of problems. The development and improvement of the autonomous navigation method without the use of GPS is an actual issue, and this issue is considered in the article. The subject of study in the article is the issue of UAVs navigation based on video images. The purpose of work to develop an algorithm for autonomous navigation of UAVs that requires less resources. The following tasks are solved in the article: software modules are developed based on image processing algorithms; suitable model for invariant identification of curves was studied and applied. The following results were obtained: an algorithm was developed for the invariant identification of winding roads (curves) without intersections from different angles; developed software modules have been tested in the Republic of Azerbaijan on the basis of a map of the region called "Agsu Pass". Conclusions: the application of the method proposed in the article will enable rotation-invariant autonomous navigation of UAVs using less resources.

Multimodal fusion for autonomous navigation via deep reinforcement learning with sparse rewards and hindsight experience replay

The multimodal perception of intelligent robots is essential for achieving collision-free and efficient navigation. Autonomous navigation is enormously challenging when perception is acquired using only vision or LiDAR sensor data due to the lack of complementary information from different sensors. This paper proposes a simple yet efficient deep reinforcement learning (DRL) with sparse rewards and hindsight experience replay (HER) to achieve multimodal navigation. By adopting the depth images and pseudo-LiDAR data generated by an RGB-D camera as input, a multimodal fusion scheme is used to enhance the perception of the surrounding environment compared to using a single sensor. To alleviate the misleading way for the agent to navigate with dense rewards, the sparse rewards are intended to identify its tasks. Additionally, the HER technique is introduced to address the sparse reward navigation issue for accelerating optimal policy learning. The results show that the proposed model achieves state-of-the-art performance in terms of success, crash, and timeout rates, as well as generalization capability.