Mobile Navigation Research Articles

Research on the Application of Mobile Navigation System Based on Cloud Computing

At present, with the increasing demand of users for product experience, it is very important to improve the user experience of mobile guided products. The purpose of this paper is to apply the user experience design ideas and methods to the design and development of mobile guide system through cloud computing, and to explore the target user’s acceptance of the user experience of the computer system. Based on these research purposes, this paper analyzed the fit point between mobile guide and user needs, the construction process of user experience design of mobile guide system, and the implementation of user experience design process in actual research and development, which provided theoretical basis and practical methods for improving product user experience and user satisfaction.

Empirical analysis on human dynamics of sharing-bicycles’ user behavior

With the development of mobile communication and global positioning system navigation and positioning technology, analysis of user behavior on mobile Internet has become a hot topic in research area. Sign in sharing-bicycles’ app, find bicycle location, and selected has become a part of mobile Internet user’s daily life. Based on the data analysis of the spatial and temporal characteristics, find out sharing-bicycle’s user behavior obeys power-law distribution. In the time interval, user behavior of sharing-bicycle has strong intermittency and weak memory; the exponent of probability that K edge nodes is three by fitting the distance of sharing-bicycle’s data curve. It is verified that mobile Internet is long to scale-free networks. We conclude seven characteristics of user’s behavior of sharing-bicycle in mobile Internet application from experimental results. The analysis of sharing-bicycle’s behavior has become a complement and extension in human dynamics research field.

A Panoramic Localizer Based on Coarse-to-Fine Descriptors for Navigation Assistance.

Visual Place Recognition (VPR) addresses visual instance retrieval tasks against discrepant scenes and gives precise localization. During a traverse, the captured images (query images) would be traced back to the already existing positions in the database images, rendering vehicles or pedestrian navigation devices distinguish ambient environments. Unfortunately, diverse appearance variations can bring about huge challenges for VPR, such as illumination changing, viewpoint varying, seasonal cycling, disparate traverses (forward and backward), and so on. In addition, the majority of current VPR algorithms are designed for forward-facing images, which can only provide with narrow Field of View (FoV) and come with severe viewpoint influences. In this paper, we propose a panoramic localizer, which is based on coarse-to-fine descriptors, leveraging panoramas for omnidirectional perception and sufficient FoV up to 360. We adopt NetVLAD descriptors in the coarse matching in a panorama-to-panorama way, for their robust performances in distinguishing different appearances, utilizing Geodesc keypoint descriptors in the fine stage in the meantime, for their capacity of detecting detailed information, formatting powerful coarse-to-fine descriptors. A comprehensive set of experiments is conducted on several datasets including both public benchmarks and our real-world campus scenes. Our system is proved to be with high recall and strong generalization capacity across various appearances. The proposed panoramic localizer can be integrated into mobile navigation devices, available for a variety of localization application scenarios.

Drivers’ acceptance of mobile navigation applications: An extended technology acceptance model considering drivers’ sense of direction, navigation application affinity and distraction perception

Abstract This study proposes an integrated technology acceptance model to investigate the factors that affect drivers’ usage intention of mobile navigation applications. The proposed model adds three new constructs (drivers’ sense of direction, navigation application affinity and distraction perception) to the original technology acceptance model based on the features of mobile navigation applications. First, a questionnaire was developed and administered, and data from 384 drivers were collected via an online survey. Second, confirmatory factor analysis was conducted to examine the reliability and validity of the developed scale based on the collected data. Third, a structural equation model was constructed to investigate the interrelationships among these constructs in the conceptual research model and to identify the key factors that affect drivers’ acceptance of mobile navigation applications. The proposed model explained 60.50% of the variance in the intention to use mobile navigation applications. In addition to attitude and perceived usefulness, navigation application affinity and distraction perception also significantly affected drivers’ intention to use mobile navigation applications. Navigation application affinity and distraction perception affected not only drivers’ intention to use but also their perceptions. Sense of direction was a significant individual trait that affected drivers’ navigation application affinity, distraction perception, perceived ease of use and perceived usefulness. These findings imply that relevant developers should continually optimize the incorrect and inappropriate use of navigation information and that they should attach importance to the amount and intelligibility of navigation information. Furthermore, the prompt form of navigation information should satisfy the demands and expectations of drivers with different senses of direction. Overall, this study improves our understanding of drivers’ acceptance of mobile navigation applications and provides some important practical implications to improve mobile navigation services.

Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living

Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encourage data sharing and mutual assistance is proposed. The game is called “Lemmings,” which stands for location-based mutual and mobile information navigation system; it is based on a classical video game where a group of creatures have to work and win through the puzzle game together. This game includes an asynchronized messaging system where a player may proactively seek for answers or advice by depositing a question on the messaging server. The server will automatically disseminate the question, which is related to a specific location, to a group of users who are either within the proximity currently or have just recently been there. The users/players are encouraged to help each other in post-pandemic Corona virus period; karma scoring distinguishes the most helpful users in the community.

A Study on Satisfaction with and Use Behavior of Mobile Navigation Service

A Study on Satisfaction with and Use Behavior of Mobile Navigation Service

Robot target localization and interactive multi-mode motion trajectory tracking based on adaptive iterative learning

Due to factors such as nonlinearity, parameter and disturbance uncertainty, actuator failure and other factors in the robot control system, it is difficult to ensure high-precision trajectory tracking of the system based on the traditional system model-based algorithm. In this paper, a method of robot vision localization based on iterative Kalman particle filter is proposed, which realizes the global positioning of the robot and carries out experimental verification. The results show that the positioning accuracy and real-time performance of the robot mobile navigation can meet the requirements. The task of performing industrial robots is repetitive, and iterative learning control technology is introduced to adapt to the repeated dynamic characteristics of industrial robots when they repeatedly perform work tasks. An adaptive iterative learning interactive multi-mode trajectory tracking controller is proposed. According to the error and error derivative of the trajectory tracking, the learning amount is obtained, which is used to correct the output of the PD controller, thereby improving the trajectory tracking accuracy. The feasibility and effectiveness of the proposed algorithm are verified by simulation results.

Light detection and ranging/inertial measurement unit-integrated navigation positioning for indoor mobile robots

To overcome the problem of the low accuracy and large accumulated errors of indoor mobile navigation and positioning, a method to integrate the light detection and ranging- and inertial measurement unit-based measurement is proposed. Firstly, the voxel-scale-invariant feature transform feature extraction algorithm for light detection and ranging is studied. Then, the errors of light detection and ranging measurement due to the change of the scan plane compensated based on aiding information from inertial measurement unit. The relative position parameters and the differences of measurements of light detection and ranging at adjacent times are used to estimate the error of inertial measurement unit sensors by a Kalman filter. Several experiments are carried out in the indoor corridor and the results demonstrate that precision of the light detection and ranging/inertial measurement unit-integrated indoor mobile robot localization is higher than that of single light detection and ranging sensor.

A Software Architecture for Service Robots Manipulating Objects in Human Environments

This paper presents a software architecture for robots providing manipulation services autonomously in human environments. In an unstructured human environment, a service robot often needs to perform tasks even without human intervention and prior knowledge about tasks and environments. For autonomous execution of tasks, varied processes are necessary such as perceiving environments, representing knowledge, reasoning with the knowledge, and planning for task and motion. While developing each of the processes is important, integrating them into a working system for deployment is also important as a robotic system can bring tangible outcomes when it works in real world. However, such an architecture has been rarely realized in the literature owing to the difficulties of a full integration, deployment, understanding high-level goals without human interventions. In this work, we suggest a software architecture that integrates the components necessary to perform tasks by a real robot without human intervention. We show our architecture composed of deep learning based perception, symbolic reasoning, AI task planning, and geometric motion planning. We implement a deep neural network that produces information about the environment, which are then stored in a knowledge base. We implement a reasoner that processes the knowledge to use the result for task planning. We show our implementation of the symbolic task planner that generates a sequence of motion predicates. We implement an interface that computes geometric information necessary for motion planning to execute the symbolic task plans. We describe the deployment of the architecture through the result of lab tests and a public demonstration. The architecture is developed based on Robot Operating System (ROS) so compatible with any robot that is capable of object manipulation and mobile navigation running in ROS. We deploy the architecture to two different robot platforms to show the compatibility.

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index ( R H I ) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the R H I can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.

ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots †

Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present ‘ITC: Infused Tangential Curves’ which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential to the path, thus maintaining continuity, (2) The curves are infused in the original global path to smooth out the turns, (3) The straight segments of the global path are kept straight and only the sharp turns are smoothed, (4) Safety is embedded in the ITC trajectories and robots are guaranteed to maintain a safe distance from the obstacles, (5) The curvature of ITC curves can easily be controlled and smooth trajectories can be generated in real-time, (6) The ITC algorithm smooths the global path on a part-by-part basis thus local smoothing at one point does not affect the global path. We compare the proposed ITC algorithm with traditional interpolation based trajectory smoothing algorithms. Results show that, in case of mobile navigation in narrow corridors, ITC paths maintain a safe distance from both walls, and are easy to generate in real-time. We test the algorithm in complex scenarios to generate curves of different curvatures, while maintaining different safety thresholds from obstacles in vicinity. We mathematically discuss smooth trajectory generation for both 2D navigation of ground robots, and 3D navigation of aerial robots. We also test the algorithm in real environments with actual robots in a complex scenario of multi-robot collision avoidance. Results show that the ITC algorithm can be generated quickly and is suitable for real-world scenarios of collision avoidance in narrow corridors.

Narratives of Women Using a 24-Hour Ride-Hailing Transport System to Increase Access and Utilization of Maternal and Newborn Health Services in Rural Western Kenya: A Qualitative Study.

.Between 1990 and 2015, Kenya had a 0.9% annual reduction in maternal mortality, one of the lowest reductions globally. This slow decline was linked to the relatively low utilization of delivery services. We designed a mobile phone–enhanced 24-hour transport navigation system coupled with personalized and interactive gestation-based text messages (MAccess) to address maternal child health service utilization. The primary purpose of this analysis is to explore the ways in which pregnant and postnatal women made decisions regarding care-seeking for pregnancy and childbirth services, the processes of getting care from home to the hospital as well their perceptions on how the MAccess intervention affected their pregnancy and childbirth care-seeking and utilization experience. We conducted semistructured, individual interviews with 18 postpartum women. Participants were purposively sampled. Interviews were audiotaped, transcribed, and analyzed using thematic analysis. For participants in this study, all three delays interacted in a complex manner to affect women’s utilization of pregnancy and childbirth services. Even though women were aware of the benefits of skilled birth attendance, other health system factors such as opening hours, or health workers’ attitudes still deterred women from delivering in health facilities. The MAccess innovation was highly acceptable to women throughout pregnancy and childbirth and helped them navigate the complex and layered individual, infrastructural, and health system factors that put them at risk of adverse maternal and newborn outcomes. These findings emphasize that an integrated approach, which addresses all delays simultaneously, is important for reducing perinatal morbidity and mortality.

Mathematics base for mobile robot navigation using mirror petri net Method

The movement of the robot will be even more challenging if on the mobile journey the robot gets a lot of obstacles, there are many choices to turn left or turn right and be able to go back to the place of origin, for that purpose precise and fast navigation is needed. This research offer new methods robot mobile navigation modeled using the mirror petri net method. This method is the development of the petri net method, which is a graphical and mathematical modeling method that is very suitable for modeling a contradictory movement system. The final result will be obtained a mathematical model with the same marking value, which is used as a mobile robot navigation. This same marking value will have a different meaning when departing and when return.

The effects of 3D interface metaphor on older adults’ mobile navigation performance and subjective evaluation

Navigating vast information spaces through mobile interfaces has become a common activity in older adults' everyday lives. Studies suggested that interface metaphors could be used to facilitate users' metal model development and information processing when using mobile technologies. However, we know little about how metaphors affect older adults' mobile navigation behavior, and which user characteristics matter during this perceptual and cognitive process. To investigate this, a card interface with a 3D metaphor and a list interface without 3D metaphors were compared among twenty-two participants when performing four navigation tasks. User characteristics including demographic factors, technology experience, and user capabilities were examined. The participants' navigation performance and subjective evaluations were measured as the dependent variables. From the results, we recommend the list interface without 3D metaphors as a beneficial choice for older adults. It performed better in navigation performance, although the differences are not statistically significant. Moreover, navigation performance using the card interface with a 3D metaphor was significantly associated with participants' perceptual speed, thus this interface may be more sensitive to capability declines. Valuable insights into the older adults’ mobile navigation performance and preferences are discussed and important implications for the design of mobile navigation user interfaces are proposed based on the results. Relevance to industryThe experimental results propose a more beneficial way to present contents on a mobile user interface for older adults and provide valuable insights for the designers and industry to help them understand the older adults’ usage and perceptions towards the application of 3D metaphors when navigating with mobile interfaces.

Proposal of active control switching alarm system in partial autonomous driving environment -Focused on mobile navigation sound alarm-

Proposal of active control switching alarm system in partial autonomous driving environment -Focused on mobile navigation sound alarm-

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

Expressway bottleneck pattern identification using traffic big data—The case of ring roads in Beijing, China

How to efficiently identify traffic bottlenecks in the expressway network and implement targeted measures is an important issue to mitigate traffic congestion. In recent years, with the proliferation of smartphones, smartphone-based navigation applications are receiving the worldwide popularity, which are being treated as an extremely rich source of traffic data. In this paper, we are devoted to proposing a novel approach to investigate traffic state and identify recurrent bottlenecks quickly and accurately from macroscopic network perspective, through fusing the data sources of fixed detectors and mobile navigation apps. First of all, this paper plots flow-speed fundamental diagram to derive critical speed, which is treated as the criterion to determine traffic state. Once the criterion has been established, typical bottlenecks in the entire expressway network can be efficiently identified through only utilizing smartphone-based probe speed data. Secondly, three pioneering indicators are put forward to quantify oversaturated traffic state and classify bottleneck patterns on urban expressway network. Applying this methodology, we take Beijing expressway network as a case to identify different patterns of bottlenecks, which are validated to comply with the reality. Moreover, the relationship between critical speed and the associated road segment features is explored and shows the possibility of predicting the critical speed even without flow data. As an end, some recommendations are proposed for improving different types of traffic bottlenecks.

Construction of potential fields in mobile robot local navigation

Construction of potential fields in mobile robot local navigation

Hybrid Path Selection Modeling by Considering Habits and Traffic Conditions

Efficient path guidance is one of the effective ways to improve the utilization of road resources and relieve traffic congestion. Therefore, it is important to identify ways to enable drivers to select path more efficiently. In this paper, we seek to build a pre + during-trip path prediction model based on the impacts of external and internal information on path selection behaviors to enhance path selection efficiency. The prediction process is composed of three parts, namely, pre-trip path prediction, during-trip link prediction, and path adjustment calculation. In the pre-trip path prediction, through RP survey data collected in Changchun, China, we determine the impacts of subjective factor (habits) on path selection using the Markov model. While the Binary Logit model is utilized by the during-trip link prediction to consider the impacts of both subjective factor (travel habits) and objective factor (real-time traffic conditions). The influences of habits and traffic conditions are compared and analyzed. The results indicate that subjective factor has more important influence than objective one. In addition, the verification results also suggest that pre + during-trip path prediction model provides higher forecasting accuracy than the single pre-trip prediction model. These findings are beneficial to uncover the underlying mechanisms of path selection and facilitate the development of strategies to enhance path selection efficiency. Based on the study results, the subjective and objective information that was found to affect path selection can be considered into the path guide system. Moreover, the hybrid prediction model can be applied in the vehicle or mobile navigation App to facilitate the recommendation of the path to travelers as well as to forecast the short-term traffic flow and determine the potential congestion area. Based on the pre-trip path prediction model, the path and the traffic flow distribution in road network can be obtained only from historical travel data.