Mobile Navigation Research Articles

Traffic jam by GPS: A systematic analysis of the negative social externalities of large-scale navigation technologies.

The increased usage of navigation technologies has caused conflicts in local traffic management, resulting in congested residential areas among other challenges for residents. This paper uses content analysis to investigate such negative social externalities within local communities and neighbourhoods. Through a corpus of 90 news articles about traffic incidents caused by navigation technologies, we identified negative traffic and safety-related externalities, including congestion, damage, pollution, and accidents. We also report on countermeasures by local communities and governments, including street closures, speed limit reduction, and turn bans. Based on our results, we discuss the implications for designing mobile navigation technologies that reduce negative social externalities.

NavigateMe : An In-Building Navigation Application

For some people to reach their destination and complete their daily tasks, navigation systems serve as an aid, a tool, or an assistance. Currently, International Islamic University Malaysia (IIUM) lacks an in-building navigation system that could assist users, particularly new students, in finding their way to the location of their choice. This paper has two goals. This study first examines the current campus navigation systems and the relevant research article. Second, this work suggests a mobile navigation system that facilitates user navigation within Kuliyyah of Information and Communication Technology (KICT) facilities. Methods used to build the application were according to Software Development Life Cycle (SDLC). The suggested navigation application, NavigateMe, helps users navigate within buildings by providing sequential instructions and images of landmarks along the route. It stands out for being usable even in the event of sporadic internet connectivity. In conclusion, visitors and the IIUM community, particularly the new students of KICT, will benefit from an in-building navigation application like NavigateMe to effortlessly reach their destination.

Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective.

Fast and Comfortable Robot-to-Human Handover for Mobile Cooperation Robot System.

In this work, we present a method that enables a mobile robot to hand over objects to humans efficiently and safely by combining mobile navigation with visual perception. Our robotic system can map its environment in real time and locate objects to pick up. It uses advanced algorithms to grasp objects in a way that suits human preference and employs path planning and obstacle avoidance to navigate back to the human user. The robot adjusts its movements during handover by analyzing the human's posture and movements through visual sensors, ensuring a smooth and collision-free handover. Tests of our system show that it can successfully hand over various objects to humans and adapt to changes in the human's hand position, highlighting improvements in safety and versatility for robotic handovers.

IBISWorld

In fall 2023, business librarian stalwart IBISWorld underwent a platform renovation to improve discoverability, usability, and shareability of its Industry Reports while retaining its dependable industry data and analyses. These changes have, by and large, been successful, concentrating financial data, improving mobile navigation, adding download and sharing options, and revising the reporting style to be quicker and easier for users to understand. While there are other database options for Industry Reports, IBISWorld remains both the most exhaustive, covering more than 700 North American Industry Classification System (NAICS) industries, and, with the new-look launch, the most intuitive to use, making it a key addition for institutions with business, advertising, marketing, or finance departments.

Boundary-aware value function generation for safe stochastic motion planning

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states’ borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention.

Combined navigation system for substation inspection robot based on DGPS/DR multi-sensor fusion

To realize the routine inspection work of the capacitor tower inspection operation robot on the capacitor tower in the substation, and to guarantee the accuracy and safety of the robot’s movement in the substation, a multi-sensor combination navigation system and the corresponding Kalman fusion filter are designed. Combined with the overall structure of the substation and the working environment of the inspection robot, the navigation and positioning requirements of the robot are analyzed and compared, and the multi-sensor combination positioning technology using a combination of DGPS, attitude sensors and velocity sensors is determined. The Kalman fusion filter based on the constant velocity model is used to fuse the positioning information of multiple sensors, and more accurate and reliable navigation information is obtained compared with the single-sensor positioning system. According to the working environment of the substation, an outdoor planar robot mobile navigation experiment is carried out to collect the positioning information of multiple sensors and carry out data fusion processing to verify the accuracy and robustness of multi-sensor combination navigation and positioning.

The effect of flow experience on reuse intention of mobile navigation apps: The mediating role of location-based mobile service quality

This study aims to determine the effect of consumers' flow experience on their intention to reuse mobile navigation apps and reveal the mediating role of location-based mobile service quality in this effect. Data were collected through an online survey of 513 respondents who were actively using mobile navigation apps and analyzed using structural equation modeling and mediation analysis. The results reveal that flow experience affects the intention to reuse mobile navigation apps and that location-based mobile service quality mediates this effect. When the mediating role of location-based service quality was tested, the direct effect of flow experience on reuse intention became insignificant. Information, reliability, and design quality are the most significant dimensions of location-based service quality. Considering the limited number of studies on mobile navigation services, particularly user behavior, this study contributes substantially to services marketing literature. In addition, this study provides insights for practitioners on how to design service proposals and the realization processes of mobile navigation apps.

The co-development of a mobile navigation app in an integrated care network in Ontario, Canada

Introduction: The complexity of navigating health and social care services is a common challenge to both providers and patients and their families. In the nineties, navigation programs arouse to assist cancer patients accessing services. Since then, those programs expanded to additional patient populations. Navigation programs can take various forms including virtual navigation. Virtual navigation in healthcare is a proactive process by which patients obtain information and support via internet resources to manage their illness demands.
 Approach: With a commitment to facilitate 24/7 access to its attributed population, Burlington Ontario Health Team (BOHT)-an integrated care network in Ontario- developed a mobile navigation app. Developing this app utilized experienced-based co-design approach. This approach took place via organizing a working group that included all stakeholders within the BOHT network of organizations and patient/caregiver partners. Over several months, the working group built potential personas of users, mapped their system navigation needs, created an inventory of navigation services within the network and designed the navigation app. Creation of the app was an iterative process including several testing and feedback loops to improve content and usability.
 Results: The first iteration of Burlington Navigation app was launched in Fall of 2021 accompanied by a comprehensive communication plan. The plan included digital signals at the hospital and primary care offices, social media posts and presentations to local providers including primary care physicians, community navigators. The first iteration focused on linking to health, social and community services offered by BOHT’s members and collaborators, and also services that were widely used in the community. Based on the feedback we received afterwards, the second iteration included links to provincial services. The third iteration includes capability to create a trusted account verified in the background by the same technology Interac uses. This will allow secure and seamless access to integrated services without a need for a separate username and password as well as access to the provincial patient portal
 Implications/highlights: Burlington Navigation app was the first community-based navigator app co-designed with patient partners, providers and community volunteers with lived experiences within the health and social care systems in Ontario. It resonated well with the users (providers and patients/caregivers) as local information became available at their fingertips. The success of the app allowed it to guide other OHTs on a similar digital navigator design journey. It also became the blueprint for a provincial solution for a multi-tenant navigator, which would allow multiple OHTs to showcase the local navigation information (based on geolocation) as well as provincial offerings.
 Conclusion: Sharing our journey co-developing the Burlington navigation app demonstrates that when local health integrated care networks utilize experienced-based co-design approaches to planning healthcare interventions, this has the potential to a) develop solutions that are well perceived by patients and providers and b) scale and spread these local solutions to larger-scale solutions that can improve the experiences of a wider spectrum of providers and patients.

DESIGN AND DEVELOPMENT OF AN INTEGRATED MOBILE APPLICATION COMBINING THE FEATURES OF 2GIS, ZENLY, AND GOOGLE MAPS

This article focuses on creating a universal mobile application that combines the power of 2GIS, Zenly and Google Maps to optimize user navigation and improve the location experience. This innovative application integrates the strengths of these platforms, simplifying navigation, route planning, real-time tracking of public transport and promoting social connectivity. The study covers the design, implementation and evaluation of this innovative solution, highlighting its potential to revolutionize modern location services. The main goal of the project is to create and evaluate a comprehensive mobile application that provides users with a single tool for location services, integrating the core functions of 2GIS, Zenly and Google Maps. The app's design pays attention to usability, using detailed Google Maps for accurate and up-todate location information. In summary, this study represents a significant contribution to the field of mobile navigation applications, demonstrating the potential to transform modern location services.

Enhancing Wayfinding Experience in Low-Vision Individuals through a Tailored Mobile Guidance Interface

Individuals with low vision (LV) face daily wayfinding challenges, struggling with route establishment, direction recognition, and obstacle avoidance. Mobile navigation, though commonly relied upon, often clashes with LV visual conditions, particularly central scotomas from diseases like age-related macular degeneration (AMD), hindering the relay of crucial wayfinding information. Addressing this, we introduced a novel guidance interface post-literature review. In a VR-based user study involving 25 participants simulating LV experience, our map guidance interface enabled quicker navigation, improving system usability and presence. The resulting performance paralleled that of normal-vision individuals. This study demonstrated that modifying familiar navigation interfaces effectively addresses visual impairment conflicts, enhancing outdoor navigation for individuals with LV. The modifications extend benefits, enabling comparable wayfinding ease to individuals with normal vision.

Real-Time Fire and Smoke Detection for Trajectory Planning and Navigation of a Mobile Robot

Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.

Novel annular seven tooth antenna compare its gain and return loss with circular patch antenna for mobile navigation

The aim of the study involves the design of a novel annular seven tooth antenna and comparing its gain, return loss with a circular patch. Totally 38 samples are considered for the analysis which is obtained using the G power tool by fixing the pretrained power and the error rate as 0.811 and 0.051 respectively. The total 38 samples are grouped into two namely novel annular seven tooth antenna with 19 samples and circular patch with 19 samples. The performance of the antennas is analyzed using return loss and gain. The design of novel annular seven tooth antenna produced a high gain of 7.7 dB which is more significant than the gain of circular patch antennas (CPA) for navigation application. It is found that the gain, return loss of the designed novel annular seven tooth antenna is 7.7 dB, -18.1 dB. The gain and return loss of the novel annular seven tooth antenna is improved and it is compared with circular patch. It is more significant as it has a p value less than 0.05.

Impact of Navigation Aid and Spatial Ability Skills on Wayfinding Performance and Workload in Indoor-Outdoor Campus Navigation: Challenges and Design

Wayfinding is important for everyone on a university campus to understand where they are and get to where they want to go to attend a meeting or a class. This study explores the dynamics of mobile navigation apps and the spatial ability skills of individuals on a wayfinding performance and perceived workload on a university campus wayfinding, including indoor-outdoor navigation, by focusing on three research objectives. (1) Compare the effectiveness of Google Maps (outdoor navigation app) and MazeMap (indoor-outdoor navigation app) on wayfinding performance and perceived workload in university campus wayfinding. (2) Investigate the impact of participants’ spatial ability skills on their wayfinding performance and perceived workload regardless of the used navigation app. (3) Highlight the challenges in indoor-outdoor university campus wayfinding using mobile navigation apps. To achieve this, a controlled experiment was conducted with 22 participants divided into a control (using Google Maps) and an experiment group (using MazeMap). Participants were required to complete a time-bound wayfinding task of navigating to meeting rooms in different buildings within the Gløshaugen campus of the Norwegian University of Science and Technology in Trondheim, Norway. Participants were assessed on spatial ability tests, mental workload, and wayfinding performance using a questionnaire, observation notes and a short follow-up interview about the challenges they faced in the task. The findings reveal a negative correlation between overall spatial ability score (spatial reasoning, spatial orientation, and sense of direction) and perceived workload (NASA TLX score and Subjective Workload Rating) and a negative correlation between sense of direction score and total hesitation during wayfinding task. However, no significant difference was found between the Google Maps and the MazeMap group for wayfinding performance and perceived workload. The qualitative analysis resulted in five key challenge categories in university campus wayfinding, providing implications for designing navigation systems that better facilitate indoor-outdoor campus navigation.

Tackling Safe and Efficient Multi-Agent Reinforcement Learning via Dynamic Shielding (Student Abstract)

Multi-agent Reinforcement Learning (MARL) has been increasingly used in safety-critical applications but has no safety guarantees, especially during training. In this paper, we propose dynamic shielding, a novel decentralized MARL framework to ensure safety in both training and deployment phases. Our framework leverages Shield, a reactive system running in parallel with the reinforcement learning algorithm to monitor and correct agents' behavior. In our algorithm, shields dynamically split and merge according to the environment state in order to maintain decentralization and avoid conservative behaviors while enjoying formal safety guarantees. We demonstrate the effectiveness of MARL with dynamic shielding in the mobile navigation scenario.

A survey on indoor positioning security and privacy

With rising demand for indoor location-based services (LBS) such as location-based marketing, mobile navigation, etc., there has been considerable interest in indoor positioning methods as well as their security and privacy. Current survey papers on indoor positioning methods mainly focus on positioning accuracy, whereas discussion on security and privacy considerations is limited. While there are survey papers on the security/privacy of LBS, they mainly focus on the services rather than the positioning methods. On the other hand, various survey papers on Internet of Things security/privacy mostly address device and system security. To fill the gap and complement the aforementioned survey papers, we conduct a systematic and comprehensive survey on indoor positioning security and privacy, focusing on the positioning methods. In particular, we provide the following contributions. First, based on general search (using the systematic PRISMA approach) and specific search, we study related papers published in recent years with the aim of addressing three research questions. Second, to facilitate the survey and study, we categorise the positioning methods into non-collaborative methods (i.e., proximity-based, geometric and fingerprinting methods), collaborative methods (i.e., mobile proximity-based and mobile geometric methods) and others (combining multiple technologies/methods). Third, for each method, we give an overview of the method and discuss its security and privacy issues. Last but not least, we highlight some future research directions and work on indoor positioning security and privacy. In particular, there is a need to conduct more research on collaborative positioning methods, including their security and privacy issues.

Biologically Inspired, Optical Waveguide with Isolation Layer Integrated Microlens Array for High‐Contrast Imaging

AbstractThe compound eyes of insects in nature provide many innovative perspectives for the development of bionic imaging devices. The construction of thousands of ommatidia favors compound eye imaging, and the close arrangement between each small eye generates a significant amount of optical crosstalk. This work reports an optical waveguide integrated with isolation‐layer microlens array (WIMLA) with high‐contrast, high‐resolution imaging inspired by the compound eyes of insects. This WIMLA is mainly fabricated by self‐written optical waveguide formed under ultraviolet exposure and the introduction of a black photoresist under the action of capillary force to achieve the effect of double isolation. The experimental results demonstrate that image contrast increases by 59.8% with double‐layer isolation and the full width at half‐maximum reduces by 27.7% in comparison with those of the MLA without any isolation layer. As a result, a more focused optical path can be confined inside the microlens for transmission. Panoramic stitching is accomplished by traversing all potential matching subimages with the edge detection and breadth‐first search algorithms. The combination of the prepared WIMLA with commercial cameras to capture faces will open up new possibilities for mobile navigation, face recognition, and medical monitoring.

Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day in the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present a sensitivity study on the dynamic rerouting parameters, discuss the simulator’s parallel scalability, and analyze system-level impacts of changing the dynamic rerouting penetration. Furthermore, we examine the varying effects on different functional classes and geographical regions and present a validation of the simulation results compared to real-world data.

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Accuracy of estimated time of arrival (ETA) for mobile vehicle tracking applications (apps) remains as a key issue to be discussed. Most of the mobile vehicle tracking apps such as WAZE is using GPS to track the current location of the vehicles. However, the accuracy of data provided by the mobile apps and factors affecting the accuracy remain as an unexplored area. This study examines the ETA accuracy by comparing ETA of a self-developed vehicle tracking app namely UCSITravel and a popular navigation app namely WAZE. The comparison test is performed by using t-test to determine if there is a statistically significant difference between the means of the datasets. Factors affecting ETA accuracy are determined in this study. The comparison results show that UCSITravel, which uses mean-value calculation (MAP), produces as accurate ETA as WAZE, which calculates ETA based on historic and real-time crowdsourced traffic data. The results shed light to mobile application developers to explore and understand more options of prediction models in developing mobile vehicle tracking app. In addition, this study has demonstrated several factors that were found to affect the accuracy of ETA in both apps as in line with literature. The results yield theoretical and practical implications.