Navigation Functions Research Articles

Indoor navigation map design based on spatial complexity

ABSTRACT In the face of the complex interior structure of a building, indoor navigation is an important tool for people to arrive at their destination in large public buildings. In this research, we first analyze the complexity of indoor space, which mainly includes spatial entities with rich semantics, but corridors lacking in semantics, poor visibility, and multi-dimensional floors. Then, we designed various indoor navigation maps based on spatial complexity and conducted a comparative analysis between the existing maps and the ones we designed. Participants were recruited to perform three navigation tasks in the study area on-site. The results demonstrate the advantages of using a hierarchical landmark representation for improved spatial knowledge acquisition and the effectiveness of scene-enhanced directional guidance in areas with poor visibility. It is worth noting that the introduction of three-dimensional maps was found to have a positive impact on users’ spatial perception and their ability to sketch routes accurately. In summary, this evidence confirms that maps based on spatial complexity not only enhance navigation efficiency but also have the potential to improve the overall indoor navigation experience.

Review Paper: Key Points on Robot Navigation and Its Practical Uses in the Field of Manufacturing

Key aspects are covered in this review article, along with a brief explanation of mobile robot navigation and its uses in the industrial sector. This study emphasizes the significance of robot navigation in enhancing productivity, efficiency, and safety in manufacturing processes by compiling important ideas from the body of research and literature. This study investigates several robot navigation algorithms and strategies, from simple algorithms to sophisticated ones like SLAM (Simultaneous Localization and Mapping). This study also examines particular issues and concerns about the application of robot navigation systems in industrial settings, such as path planning, obstacle avoidance, and worker cooperation. This paper presents some noted applications of robot navigation, such as material handling, assembly, quality control, and logistics, using case studies and examples. The conversation also touches on new developments and prospective paths for robot navigation technology, highlighting the possibility for more innovation and connection with Industry 4.0 projects. All things considered, this review article is an invaluable tool for scholars, practitioners, and business experts who want to comprehend the function of robot navigation in contemporary manufacturing processes and how it will affect industrial automation in the future.

Long-term navigation for autonomous robots based on spatio-temporal map prediction

The robotics community has witnessed a growing demand for long-term navigation of autonomous robots in diverse environments, including factories, homes, offices, and public places. The core challenge in long-term navigation for autonomous robots lies in effectively adapting to varying degrees of dynamism in the environment. In this paper, we propose a long-term navigation method for autonomous robots based on spatio-temporal map prediction. The time series model is introduced to learn the changing patterns of different environmental structures or objects on multiple time scales based on the historical maps and forecast the future maps for long-term navigation. Then, an improved global path planning algorithm is performed based on the time-variant predicted cost maps. During navigation, the current observations are fused with the predicted map through a modified Bayesian filter to reduce the impact of prediction errors, and the updated map is stored for future predictions. We run simulation and conduct several weeks of experiments in multiple scenarios. The results show that our algorithm is effective and robust for long-term navigation in dynamic environments.

Роль цифровизации в развитии экономико-туристического пространства региона: опыт Мурманской области

Introduction. The Murmansk region is the pearl of Arctic tourism, and its tourist and recreational potential is considered in the work. Theoretical analysis. During the theoretical analysis, it was found that a tourist multiplier operates in the economy of the Murmansk region, contributing to the development of the regional tourism industry. At the same time, the acceleration effect in the industry is observed due to the introduction of digital technologies. Empirical analysis. The analysis of the tourism industry economic indicators the Murmansk region gave mixed results. However, the dynamics of many indicators is positive, although it cannot be unequivocally stated that the tourism industry is the flagship industry of the region. At the same time, there is an active application of digitalization achievements in the region, although insufficient. Regional tourist industry facilities use virtual excursions as promotion tools. However, other digitalization mechanisms will also contribute to the popularization of the Murmansk region. In particular, they include conducting excursions through live broadcasts for people with disabilities, producing games with the landscapes of the Kola Peninsula, creating an application with a built-in navigation function along a pre-laid route, and, in the future, building personal routes by amateur tourists independently using this application. Results. Proposals for the introduction of digital technologies into the activities of accommodation facilities, the use of games with locations in the region, and the increase in excursion streams can lead to an acceleration of the cartoon effect in the Murmansk region and increase the tourist flow to the region.

Optimizing Urban Mobility: Assessing the Impact of Selangor's Intelligent Transport System (SITs)

This paper addresses Malaysia's public transport system, primarily managed by small operators in its developing state. Efficient transportation management relies on collaborative information sharing. Systems must provide an easy and effective way to manage staff and registered users, including students and community members. Working passengers require efficient route information to minimize delays. Therefore, systems should cater to all users' needs. Registered users, such as students, also require real-time bus updates to avoid long waits and inform the organization of delays via SMS. Implementing and managing these services collectively presents challenges. Mobile technology, including smartphones and laptops, offers a solution through location-based applications for managing transportation with map navigation. The recent mobile technology revolution has intensified competition among developers, resulting in similar yet distinct applications. Modern transit services now offer virtual access to schedules and advanced features like integration with Google Maps and real-time tracking. De-spite benefits, implementing a Smart Transportation Application (STA) with GPS and digital route maps can be daunting and costly for smaller operators.

Decentralized Navigation with Optimality for Multiple Holonomic Agents in Simply Connected Workspaces.

Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory. To achieve this, we design a decentralized control protocol, defined by a navigation function, where each agent is equipped with a navigation controller that resolves imminent safety conflicts with the others, as well as the workspace boundary, without requesting knowledge about the goal position of the other agents. Our approach is rendered sub-optimal, since each agent owns a predetermined optimal policy calculated by a novel off-policy iterative method. We use this method because the computational complexity of learning-based methods needed to calculate the global optimal solution becomes unrealistic as the number of agents increases. To achieve our goal, we examine how much the yielded sub-optimal trajectory deviates from the optimal one and how much time the multi-agent system needs to accomplish its task as we increase the number of agents. Finally, we compare our method results with a discrete centralized policy method, also known as a Multi-Agent Poli-RRT* algorithm, to demonstrate the validity of our method when it is attached to other research algorithms.

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them.

NAVIDRO, a CARES architectural style for configuring drone co-simulation

One primary objective of drone simulation is to evaluate diverse drone configurations and contexts aligned with specific user objectives. The initial challenge for simulator designers involves managing the heterogeneity of drone components, encompassing both software and hardware systems, as well as the drone’s behavior. To facilitate the integration of these diverse models, the Functional Mock-Up Interface (FMI) for co-simulation proposes a generic data-oriented interface. However, an additional challenge lies in simplifying the configuration of co-simulation, necessitating an approach to guide the modeling of parametric features and operational conditions such as failures or environment changes. The article addresses this challenge by introducing CARES, a model-driven engineering and component-based approach for designing drone simulators, integrating the FMI for co-simulation. The proposed models incorporate concepts from component-based software engineering and FMI. The NAVIDRO architectural style is presented for designing and configuring drone co-simulation. CARES utilizes a code generator to produce structural glue code (Java or C++), facilitating the integration of FMI-based domain-specific code. The approach is evaluated through the development of a simulator for navigation functions in an autonomous underwater vehicle, demonstrating its effectiveness in assessing various autonomous underwater vehicle configurations and contexts.

Convergent wheeled robot navigation based on an interpolated potential function and gradient

The article presents a novel idea to construct a smooth navigation function for a wheeled robot based on grid-based search, that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predictive control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot.

The emergence of enhanced intelligence in a brain-inspired cognitive architecture.

The Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of "navigation maps." An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.

DRONE POWERED CLASSROOM PRESENCE TRACKER

In today's rapidly evolving educational landscape, the traditional methods of manual attendance tracking in classrooms are proving to be increasingly inefficient and prone to inaccuracies. This project introduces an innovative solution to modernize classroom attendance tracking through the integration of drone technology and advanced computer vision algorithms. By combining autonomous navigation, object detection, data processing, real-time reporting, and user interface functionalities, the system streamlines attendance management processes in educational environments. Utilizing drones equipped with advanced computer vision algorithms like YOLOv4, the system autonomously navigates classroom spaces to accurately detect and count individuals in real-time. The captured attendance data undergoes processing to filter irrelevant information, enabling educators and administrators to access up-to-date attendance information instantly through real-time reporting features. The user-friendly interface enhances accessibility and usability, making the system easily adaptable to educational settings of varying sizes and layouts. Additionally, the project's flexibility, precision, and proactive monitoring capabilities contribute to a more efficient, accurate, and proactive approach to attendance management, ultimately improving student engagement and success in the classroom. The proactive monitoring enabled by real-time reporting features allows educators to identify attendance trends promptly and intervene as needed to support student engagement and success. Keywords: Drone Technology, Computer Vision, Object Detection, YOLOv4, Real Time Reporting, Attendance Tracking

The Ashwell Project: creating an online geospatial community

As the world increasingly embraces digital platforms, archaeologists are adapting their methods of public engagement accordingly. This was particularly evident during the COVID-19 pandemic, when many outreach and engagement efforts moved online. One such project was The Ashwell Project (TAP), which combined aspects of participatory Geographical Information Systems (GIS) and crowdsourcing of datasets, with Progressive Web App functionality of geolocation and navigation to disseminate community-collected photographs and narratives. The project's primary focus was two-fold: to disseminate anecdotal datasets within local heritage initiatives, and how to facilitate the involvement of users with limited technical proficiency innavigating complex digital systems. This paper highlights the opportunities and valuable lessons regarding digital engagement in communities. It considers strategies to promote the adoption of participatory GIS and crowdsourcing datasets, as well as how users' own devices can be utilised to increase engagement with tangible and intangible heritage. I argue that such approaches merit broader consideration, encouraging communities to actively engage with such platforms. The project underscores the importance of design thinking, emphasising empathy and iterative testing, in crafting effective heritage assets. Furthermore, it demonstrates the feasibility of engaging the public with archaeology even amidst a global pandemic.

Indoor Navigation App Using Navigine

Indoor navigation systems are increasingly needed for smart cities, robots, and visually impaired people. Although Global Positioning System (GPS) is widely used for navigation in numerous applications, it cannot be used for indoor navigation systems as the signals cannot be absorbed by the walls of buildings. Therefore, various indoor positioning and navigation techniques have been proposed in the literature, such as radio frequency, computer vision, sensor-based, and semantic-based solutions. However, most of these techniques suffer from high cost, low accuracy, or limited scalability. The 2-dimensional navigation system uses the floor map of the building and it directs the user with the help of a route in addition, the system guides with a path assistance mechanism. Various technologies such as Bluetooth® Low Energy, Wi-Fi, etc., can be used for implementation. These allow for determining the location with an accuracy of 3-7 meters and according to the capability of the device. Key Words: Bluetooth Low Energy(BLE), Global Positioning System(GPS), Indoor Positioning Navigation System(IPNS), Simultaneous Localization and Mapping Technology(SLAM).

5G interference with aviation altimeters: technology and policy recommendations for coexistence

The fifth generation (5G) cellular network roll-out provides high-speed performance, better coverage, and ubiquitous connectivity for the next-generation of intelligent communication systems. Compared to earlier versions, the new 5G wireless standard operates across a wider range of frequencies, which has raised aviation industry's concerns regarding safety and compatibility. For example, 5G systems operating with frequencies that are adjacent those used by radio altimeters could disrupt flight operations and navigation functions. This paper provides an overview of current research efforts assessing the 5G interference and its potential operational impact on altimeters. The work discusses the current stance of regulators and telecommunications operators worldwide on potential 5G interference with altimeters. The paper also compares the ongoing public consultations with key stakeholders globally regarding cellular systems' interference with altimeters. This work offers technical and policy recommendations to spectrum regulators and aviation authorities to inform the safe and efficient deployment of 5G spectra. This work identifies long-term solutions for achieving maximum 5G bands usability and ensuring compatibility among cellular technologies and aviation systems.

Multivariate Approach for Texture Segmentation using Probabilistic Statistical Model

The analysis of the regions of the image is of the prerogatives in the fields of medical and global systems meant for location identification. This analysis is strongly associated with partitions of regions of interest such as segmentation. For an effective strategy of analyzing the regions of interest, texture of the image plays a major concern. The texture is generally characterized using signal processing methods namely Discrete Cosine Transformation coefficients and their specific insights leading to feature vector selection. Further, to identify regions, a statistical model needs to be identified for the feature matrix vector and thus make use of Gaussian mixture model with extensions. The Expectation Maximization approach is used, and performance is assessed by experimenting with random images from the Brodatz data store domain. Performance measurements for texture segmentation that can be attributed are Global Cons. Error (GC), Prob. Rand. Index (PR) and Variation of data (VA). These are determined alongside the confusion matrix. To assess the improvement, a comparison was made with other existing models and showed better. The algorithms will be exceptionally helpful for clinical analysis and in radio navigation map systems.

Implementation of Intelligent Indoor Service Robot Based on ROS and Deep Learning

When faced with challenges such as adapting to dynamic environments and handling ambiguous identification, indoor service robots encounter manifold difficulties. This paper aims to address this issue by proposing the design of a service robot equipped with precise small-object recognition, autonomous path planning, and obstacle-avoidance capabilities. We conducted in-depth research on the suitability of three SLAM algorithms (GMapping, Hector-SLAM, and Cartographer) in indoor environments and explored their performance disparities. Upon this foundation, we have elected to utilize the STM32F407VET6 and Nvidia Jetson Nano B01 as our processing controllers. For the program design on the STM32 side, we are employing the FreeRTOS operating system, while for the Jetson Nano side, we are employing ROS (Robot Operating System) for program design. The robot employs a differential drive chassis, enabling successful autonomous path planning and obstacle-avoidance maneuvers. Within indoor environments, we utilized the YOLOv3 algorithm for target detection, achieving precise target identification. Through a series of simulations and real-world experiments, we validated the performance and feasibility of the robot, including mapping, navigation, and target detection functionalities. Experimental results demonstrate the robot’s outstanding performance and accuracy in indoor environments, offering users efficient service and presenting new avenues and methodologies for the development of indoor service robots.

Computing Metric Dimension of Two Types of Claw-free Cubic Graphs with Applications

Consider the simple connected graph G with vertex set V(G) and edge set E(G). A graph \(G\) can be resolved by \(R\) if each vertex’s representation of distances to the other vertices in \(R\) uniquely identifies it. The minimum cardinality of the set \(R\) is the metric dimension of \(G\). The length of the shortest path between any two vertices, x, y in V(G), is signified by the distance symbol d(x, y). An ordered k-tuple \(r(x/R)=(d(x,z_1),d(x,\ z_2),…,d(x,z_k))\) represents representation of \(x\) with respect to \(R\) for an ordered subset \(R={\{z}_1,z_2,z_3…,z_k\}\) of vertices and vertex \(x\) in a connected graph. Metric dimension is used in a wide range of contexts where connection, distance, and connectedness are essential factors. It facilitates understanding the structure and dynamics of complex networks and problems relating to robotics network design, navigation, optimization, and facility location. Robots can optimize their localization and navigation methods using a small number of reference sites due to the pertinent idea of metric dimension. As a result, many robotic applications, such as collaborative robotics, autonomous navigation, and environment mapping, are more accurate, efficient, and resilient. A claw-free cubic graph (CCG) is one in which no induced subgraph is a claw. CCG proves helpful in various fields, including optimization, network design, and algorithm development. They offer intriguing structural and algorithmic properties. Developing algorithms and results for claw-free graphs frequently has applications in solving of challenging real-world situations. The metric dimension of a couple of claw-free cubic graphs (CCG), a string of diamonds (SOD), and a ring of diamonds (ROD) will be determined in this work.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

Dynamic Projection Method of Electronic Navigational Charts for Polar Navigation

Electronic navigational charts (ENCs) are geospatial databases compiled in strict accordance with the technical specifications of the International Hydrographic Organization (IHO). Electronic Chart Display and Information System (ECDIS) is a Geographic Information System (GIS) operated by ENCs for real-time navigation at sea, which is one of the key technologies for intelligent ships to realize autonomous navigation, intelligent decision-making, and other functions. Facing the urgent demand for high-precision and real-time nautical chart products for polar navigation under the new situation, the projection of ENCs for polar navigation is systematically analyzed in this paper. Based on the theory of complex functions, we derive direct transformations of Mercator projection, polar Gauss-Krüger projection, and polar stereographic projection. A rational set of dynamic projection options oriented towards polar navigation is proposed with reference to existing specifications for the compilation of the ENCs. From the perspective of nautical users, rather than the GIS expert or professional cartographer, an ENCs visualization idea based on multithread-double buffering is integrated into Polar Region Electronic Navigational Charts software, which effectively solves the problem of large projection distortion in polar navigation applications. Taking the CGCS2000 reference ellipsoid as an example, the numerical analysis shows that the length distortion of the Mercator projection is less than 10% in the region up to 74°, but it is more than 80% at very high latitudes. The maximum distortion of the polar Gauss-Krüger projection does not exceed 10%. The degree of distortion of the polar stereographic projection is less than 1% above 79°. In addition, the computational errors of the direct conversion formulas do not exceed 10−9 m throughout the Arctic range. From the point of view of the computational efficiency of the direct conversion model, it takes no more than 0.1 s to compute nearly 8 million points at 1′×1′ resolution, which fully meets the demand for real-time nautical chart products under information technology conditions.