Intelligent Navigation System Research Articles

Intelligent Navigation System based on Big Data Traffic System

This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.

Synthetic Maritime Traffic Generation System for Performance Verification of Maritime Autonomous Surface Ships

With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS’s ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance.

Microelectronics innovation and implementation in intelligent transportation systems

Under the background of urbanization and rapid development of transportation, the innovation of intelligent transportation system has become the key to improving traffic efficiency, relieving traffic pressure, and solving traffic problems. With the continuous progress of microelectronics technology, its application in the field of intelligent transportation is becoming more and more eye-catching. This paper focuses on the innovation and realization of micro-electronic technology in intelligent transportation system, and discusses the application of micro-electronic technology in intelligent navigation, intelligent parking, traffic flow optimization, etc. Through a literature review approach, this study demonstrates how microelectronics technology can drive the development of intelligent transportation systems to improve the efficiency and sustainability of urban transportation. The research results show that microelectronics technology not only brings revolutionary changes to the field of intelligent transportation, but also provides accurate positioning and navigation functions in intelligent navigation systems, realizes more efficient parking process management in intelligent parking systems, and plays a key role in traffic flow optimization. Microelectronic technology has wide application prospects and a positive social influence in the field of intelligent transportation.

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

Visual–Inertial Navigation System Based on Virtual Inertial Sensors

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.

Implementation of Intelligent Navigation System for Shopping using Smart Robots

The development of a path finding algorithm-based intelligent navigation system for a smart robot shopping cart is an exciting project that has the potential to revolutionize the retail industry. With the integration of advanced path finding algorithms, the system will be able to navigate through the store independently, avoiding obstacles and dynamically changing its course to provide customers with a seamless shopping experience. The inclusion of personalized product recommendations and store layout details will help customers find the products they need more quickly and efficiently. Additionally, the system's user interface will provide an interactive shopping experience for customers, making the process more engaging and enjoyable. By automating the shopping cart, the system will also alleviate the workload of store personnel, freeing them up to focus on other important tasks. This could lead to a more efficient and cost-effective retail operation, which may translate into cost savings for both the retailer and the customer. Overall, the successful implementation of the project could significantly enhance the shopping experience for customers, potentially leading to increased customer satisfaction and loyalty. The project could also have broader implications for the retail sector, as other retailers may seek to adopt similar technology to remain competitive.

A search and rescue robot device realization based on ROS operating system combined with visual algorithm

Aiming at the problem of low accuracy of intelligent devices to find missing people, a search and rescue robot device based on ROS intelligent navigation system was proposed. SolidWorks was used to improve the new mechanical structure that can switch autonomously to different terrain and combined with Yolov5 deep learning algorithm to complete face recognition. Using the stm32 single-chip microcomputer, we adjusted the cascade PID parameters of the motor and simulation to realize the omnidirectional movement control of the robot. Through the analysis and verification of the experimental data, the adaptive ability of the improved robot platform to the terrain was significantly improved. The method can accurately complete the task of face recognition and autonomous navigation, which is practical and improves the accuracy of finding the missing people.

Dynamic Risk Analysis of Intelligent Navigation Process Based on Dynamic Bayesian Network

With the rise and development of the concept of autonomous ships, autonomous ships have become a research hotspot in the field of international maritime and transportation. The intelligent navigation system has attracted much attention as a typical function of autonomous ships. During navigation, new risks are inevitable due to the separation from human control, so carrying out a dynamic risk analysis is of great significance. This paper is based on the risk influencing factors and typical risk events during navigation. Then, the Dynamic Bayesian Network model is established by using risk factors and typical risk events, and the risk value of each risk factor is determined. It is substituted into the model to calculate and predict the current and future risk values.

Research on Bluetooth AOA-based Indoor Navigation System for Hospitals

Bluetooth 5.1 introduces the direction-finding function which can detect the direction of Bluetooth signal, on this basis, this project combines the actual hospital scenario and uses Bluetooth AOA method and IoT technology to design the implementation of intelligent navigation system in hospital. The project uses WebGL technology to provide real-time cross-floor hybrid spatial guidance navigation for patients by interfacing with the hospital's HIS and realizing in-hospital intelligent navigation with the help of WeChat applets to help patients quickly and accurately locate their consultation locations. The application of the hospital indoor navigation system can shorten patients' in-hospital wayfinding time, reduce the work pressure of front desk staff, improve patients' medical experience, and effectively improve the digital management level of the hospital.

An Efficient Feature Augmentation and LSTM-Based Method to Predict Maritime Traffic Conditions

The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean.

Design and implementation of a testing platform for ship control: A case study on the optimal switching controller for ship motion

Fully autonomous navigation is the highest level of ship autonomy, which has high requirements for the adaptability of the ship control system. Taking navigation with large turning errors as an example, this paper combines the intelligent optimization controller with the traditional controller and adopts the controller switching strategy to guide the ship’s path following. In order to illustrate the feasibility of optimizing the switching mode of the motion controller to reduce the large turning error during ship navigation, this paper designs a test platform for the intelligent navigation of the model ship, and tests the effect of the optimal switching strategy of the controller. Firstly, the structure of the intelligent ship system is analyzed, and the framework of the intelligent navigation system is emphasized. Secondly, based on the analysis of the characteristics of controllers, the optimal switching strategy of the motion controller is proposed. Then, the construction process of the testing platform is described. Finally, the optimal switching strategy of the motion controller is verified on this test platform for the case of a large turning error of the ship. The results show that the implementation of the switching strategy can effectively reduce the control error compared with the mode without using the strategy. The experiment extends the ship motion control mode strategically and improves the adaptability of ship motion control. Based on the testing platform, this experiment can simulate the process of the control system autonomously selecting an appropriate controller according to the ship’s motion state and navigation scenarios, which is helpful to enhance the adaptability of the ship in the face of special navigation scenarios.

Optimal Transshipment Route Planning Method Based on Deep Learning for Multimodal Transport Scenarios

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same.

Design of Mobile Intelligent Navigation System for Xi’an Qinling Wildlife Park

With the continuous development of China’s economy and the improvement of people’s living standards, the tourism industry is getting stronger and stronger that it integrates food, living, transportation, entertainment, shopping, and other aspects. Among them, the core competitiveness is tourism resources, which are the foundation for the survival of the tourism industry. Sustainable development of the tourism industry makes higher requirements for the advancement and management of tourism resources. How to better meet customers’ needs and continuously improve service quality has become the key to development. In this study, Xi’an Qinling Wildlife Park is taken as an example, and PHP + MYSQL and other technologies are used to construct a mobile intelligent navigation system with rich functions, simplicity, practicality, and stable performance. The whole system uses B/S mode, and the main development of the system has been completed from the perspectives of practical interface and rapid development.

Dynamic adaptive intelligent navigation decision making method for multi-object situation in open water

The intelligent navigation system of autonomous ships aims at making fully autonomous navigation decisions to replace the work of seafarers. Realizing intelligent navigation decision-making in open water is the first stage of a fully autonomous navigation system. Through real-time sampling, rapid update of the own ship's motion parameters, and traffic situation information, an open-water intelligent navigation decision-making method that can dynamically adapt to the residual error in the system and the target ship's random manoeuvre is proposed. A digital traffic environment model comprising different target types is built as the basis of navigation decision-making. An optimal feasible plans of the own ship, which will ensure all targets pass safely, is calculated using the velocity obstacle (VO) theory, and dynamic collision avoidance mechanism to construct an autonomous manoeuvring model. The simulation experiments proved that the combined results of this and previous research could realize autonomous navigation in an open water environment having multiple static and dynamic objects under the premise of satisfying the International Regulations for preventing Collisions at Sea, 1972 (COLREGs) and the good seamanship. This research will provide basic theories and methods to realize the autonomous navigation decision-making of ships in an open water environment with multiple targets.

Multi-agent reinforcement learning for Markov routing games: A new modeling paradigm for dynamic traffic assignment

This paper aims to develop a paradigm that models the learning behavior of intelligent agents (including but not limited to autonomous vehicles, connected and automated vehicles, or human-driven vehicles with intelligent navigation systems where human drivers follow the navigation instructions completely) with a utility-optimizing goal and the system’s equilibrating processes in a routing game among atomic selfish agents. Such a paradigm can assist policymakers in devising optimal operational and planning countermeasures under both normal and abnormal circumstances. To this end, we develop a Markov routing game (MRG) in which each agent learns and updates her own en-route path choice policy while interacting with others in transportation networks. To efficiently solve MRG, we formulate it as multi-agent reinforcement learning (MARL) and devise a mean field multi-agent deep Q learning (MF-MA-DQL) approach that captures the competition among agents. The linkage between the classical DUE paradigm and our proposed Markov routing game (MRG) is discussed. We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL). In other words, the MRG depicts DUEs assuming perfect information and deterministic environments propagated by DNL models. Four examples are solved to illustrate the algorithm efficiency and consistency between DUE and the MRG equilibrium, on a simple network without and with spillback, the Ortuzar Willumsen (OW) Network, and a real-world network near Columbia University’s campus in Manhattan of New York City.

The Sea Route Planning for Survey Vessel Intelligently Navigating to the Survey Lines.

Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle.

Toward blockchain adoption for the automotive industry

Blockchain proves to be a powerful technology that can drive forward the automotive industry. This paper describes how blockchain has been applied, which automotive areas are most interested in the technology, as well as the benefits and challenges of blockchain for the automotive industry by a systematic literature review. Blockchain offers many advantages, such as: transparency, security, high speed of operations. There are numerous areas where this technology could be applied in the automotive domain: better protection against counterfeit spare parts and materials, effective preventive maintenance, by informing the services based on the data received from the car sensors, monitoring of the cars by the rental companies, the safe storage of the data used by intelligent navigation systems, in order not to affect the safety of the passengers, autonomous transactions, triggered by the vehicle for making payments in charging stations, all with the purpose of facilitating a good and safer medium for drivers. The future vehicle will be connected both with other vehicles, but also with the existing infrastructure, so a database containing cryptographically secure information is needed, and blockchain is such a solution that can be applied in the dynamic nature of traffic.

Wireless Sensor Networks Assist Intelligent Tourism Traffic Navigation and Tourist Positioning

With the booming development of tourism, more and more tourists visit scenic spots; followed by dense personnel in scenic spots, tourists’ comfort is not high. This paper introduces the structure diagram, function structure diagram, and database design of an intelligent tourism navigation system based on a wireless sensor network in detail and explains each function module of an intelligent tourism system based on a wireless sensor network in detail. Through the wireless sensor network to obtain the positioning data of tourists in tourist attractions, this article realizes the intelligent traffic navigation in the intelligent tourism navigation system and finally realizes the diversion of tourists. An intelligent travel navigation system based on the wireless sensor network is implemented on Android. Finally, the intelligent travel navigation system based on the wireless sensor network is tested. The results show that the system can meet the real-time demand, the system positioning accuracy is high, and the system can effectively realize the flow analysis and public security warning.

Low-cost and high-performance visual guidance and navigation system for space debris removal

A highly intelligent visual guidance navigation system that can autonomously identify target space debris and realize a controlled rendezvous maneuver is required to mitigate space debris. In this study, such a system is developed by integrating autonomous control software technologies and commercial off-the-shelf (COTS) devices. The orbital performance of the COTS-based intelligent camera system of the end-of-life services is demonstrated during an Astroscale-demonstration mission, a technology demonstration mission for satellite end-of-life services. For this mission, Astroscale collaborated with the Tokyo University of Science to develop a visual guidance and navigation system for space debris Removal. In this regard, this paper reports the design and performance of a COTS-based intelligent camera system for space debris Removal.

Design on adaptive weighted guidance law for underwater intelligent vehicle tracking target

In future naval warfare, the accurate attack of underwater intelligent vehicles on targets will become more and more important. Therefore, it is very important to study the guiding method which has the key influence on tracking targets. In the guidance process of intelligent navigation system, the estimation of guidance information by the agent is influenced by confrontation and interference, which often results in the decrease of the guidance accuracy of UUV under the single guidance law. In this paper, the estimated information is classified according to the different types of guidance target information combining to the advantages of three guidance methods, namely fixed advance angle, extended proportional guidance law and adaptive sliding mode guidance law. Then the fuzzy weight coefficient calculation method is used to get the weight coefficients of all kinds of information to form the adaptive weighted guidance law. The target tracking effects of each guidance method is simulated and compared in the simulation environment of the target maneuver and the target non-maneuver respectively.