Route Navigation Research Articles

Research on the Transport of Typical Pollutants in the Yellow Sea with Flow and Wind Fields

In this study, we developed a transport model for typical pollutants in the Yellow Sea using the Lagrangian particle tracking method to analyze the trajectories of fish feed, a common pollutant in the Yellow Sea. The model incorporates the influence of ocean currents and surface winds on pollutant transport and utilizes a series of numerical experiments to simulate pollutant transport. Through statistical analysis of the numerical experiment results, we identified characteristic circles that represent the pollutant distribution patterns. Furthermore, based on the current and wind information within these characteristic circles, we derived an empirical formula to describe pollutant distribution. This formula enables us to predict the spatial distribution of pollutants using available current and wind data. Using this empirical formula, we designed an effective path to avoid pollutant contamination. This approach not only optimizes the utilization of computational resources within the study area but also contributes to the rational planning of navigation routes for aquaculture vessels. Overall, our study provides valuable insights into the transport behavior of fish feed pollutants in the Yellow Sea. The establishment of the empirical formula and the design of effective routes to avoid pollution contribute to the efficient management of pollution and facilitate the planning of marine activities in the region.

An Ensemble Filter for Indoor Positioning Technology of Mobile Home Service with Agile iBeacon Deployment

In this article, we undertake a thorough investigation into the theory of indoor location, adopt an effective and quick positioning algorithm, and make use of a network of lowpower iBeacons. An iBeacon-based indoor positioning system (IPS) is presented to investigate how to utilize iBeacon for accurate location and whether it can successfully replace the current dominant positioning technology based on the analysis that was conducted. However, the first things that should be taken into account at this point in the design of house robots for indoor environments are how to quickly and precisely gather target node location information as well as how to regulate and plan a course. This article examined the more popular and often used indoor positioning techniques, provided a succinct summary of current indoor positioning technologies and regulators, and examined ultrasonic locating technology in depth. Based on this, a system for mobile home service robots was developed, and simulation tests were performed to assess the accuracy of node locating, node reception and arrival times, the best level of route planning, and navigation and path estimation errors in both absolute and comparative terms. Additionally, we go into detail about the difficulties in developing a practical IPS, the solutions that are already available, a thorough performance comparison, and some potential future IPS development trends.

Application of an Encoder–Decoder Model with Attention Mechanism for Trajectory Prediction Based on AIS Data: Case Studies from the Yangtze River of China and the Eastern Coast of the U.S

With the rapid growth of shipping volumes, ship navigation and path planning have attracted increased attention. To design navigation routes and avoid ship collisions, accurate ship trajectory prediction based on automatic identification system data is required. Therefore, this study developed an encoder–decoder learning model for ship trajectory prediction, to avoid ship collisions. The proposed model includes long short-term memory units and an attention mechanism. Long short-term memory can extract relationships between the historical trajectory of a ship and the current state of encountered ships. Simultaneously, the global attention mechanism in the proposed model can identify interactions between the output and input trajectory sequences, and a multi-head self-attention mechanism in the proposed model is used to learn the feature fusion representation between the input trajectory sequences. Six case studies of trajectory prediction for ship collision avoidance from the Yangtze River of China and the eastern coast of the U.S. were investigated and compared. The results showed that the average mean absolute errors of our model were much lower than those of the classical neural networks and other state-of-the-art models that included attention mechanisms.

Планирование безопасных маршрутов безэкипажных судов на основе методов искусственного интеллекта

The article considers the problem of improving safety and efficiency of navigation of unmanned vessels. In the unmanned navigation, one of the most important tasks is planning a safe route, which consists in determining the shortest path in the shortest possible time and describing the motion of a marine vessel. The object of this study is the safe route of an unmanned vessel. There is given a review of the research literature on solving the problem of planning the route of sea and river mobile objects. The subject of the study is intelligent decision-making methods for safe route planning for unmanned navigation. The purpose of this article is to improve the safety of unmanned navigation by optimizing the route in the presence of one or more obstacles in the area of movement of an unmanned vessel that have 
 a negative impact on the trajectory of movement. To achieve this goal, the task of local route planning was set and solved by the method of intelligent decision-making based on Biologically Inspired Neural Network (BINN). Methods of artificial intelligence and mathematical modeling were used to solve the problem. The results of the proposed method for solving the problem of planning a local route for an unmanned vessel confirm its ability of the vessel to avoid the local minima points. The simulation results show that the planned local route for unmanned navigation allows solving the problem of avoiding collisions with obstacles in real time, where the obstacles have only local effects. As part of further research, the described method is planned to be used for developing an information decision-making system for a movement control of an unmanned vessel.

A Novel Approach for Bathymetry Estimation through Bayesian Gravity Inversion

The bathymetry is the most superficial layer of the Earth’s crust on which it is possible to perform direct measurements. However, it is also well known that water covers more than 70% of the Earth’s surface, so an enormous expenditure of acquisition campaigns should be performed to produce a high-resolution map of this layer. Currently exploiting mainly commercial navigation routes, the sea floor coverage with shipborne sounding is only at 11%, and the remaining part is currently modeled by classical interpolation techniques or satellite-based gravity inversion methods. In the present work, a new method to refine bathymetry modeling based on the exploitation of global gravity field models is presented. In the proposed solution, once modeled and removed from the observed gravity field, the gravitational signals related to the deepest structures, a 3D Bayesian inversion algorithm is used to improve the actual knowledge of bathymetry. The proposed inversion method also enables limiting the solution to shipborne sounding measurements in such a way as to improve the seafloor grid where no local, high-quality information is available. Two test cases are discussed in the Mediterranean Sea region. Promising results are presented, opening the possibility of applying an analogous method to refine the bathymetry modeling at larger scales up to the global one.

Network bypasses sustain complexity

Real-world networks are neither regular nor random, a fact elegantly explained by mechanisms such as the Watts-Strogatz or the Barabási-Albert models, among others. Both mechanisms naturally create shortcuts and hubs, which while enhancing the network's connectivity, also might yield several undesired navigational effects: They tend to be overused during geodesic navigational processes-making the networks fragile-and provide suboptimal routes for diffusive-like navigation. Why, then, networks with complex topologies are ubiquitous? Here, we unveil that these models also entropically generate network bypasses: alternative routes to shortest paths which are topologically longer but easier to navigate. We develop a mathematical theory that elucidates the emergence and consolidation of network bypasses and measure their navigability gain. We apply our theory to a wide range of real-world networks and find that they sustain complexity by different amounts of network bypasses. At the top of this complexity ranking we found the human brain, which points out the importance of these results to understand the plasticity of complex systems.

Landmark Sequence Learning from Real-World Route Navigation and the Impact of Navigation Aid Visualisation Style.

Primacy and recency features of serial memory are a hallmark of typical memory functions that have been observed for a wide array of tasks. Recently, the ubiquity of this serial position effect has been supported for objects learned during navigation, with canonical serial position functions observed for sequence memory of landmarks that were encountered along a route during a highly controlled virtual navigation task. In the present study, we extended those findings to a real-world navigation task in which participants actively walked a route through a city whilst using a navigation aid featuring either realistic or abstract landmark visualisation styles. Analyses of serial position functions (i.e., absolute sequence knowledge) and sequence lags (i.e., relative sequence knowledge) yielded similar profiles to those observed in a lab based virtual navigation task from previous work and non-spatial list learning studies. There were strong primacy effects for serial position memory in both conditions; recency effects only in the realistic visualisation condition; a non-uniform distribution of item-lags peaking at lag +1; and an overall bias towards positive lags for both visualisation conditions. The findings demonstrate that benchmark serial position memory effects can be observed in uncontrolled, real-world behaviour. In a navigation context, the results support the notion that general memory mechanisms are involved in spatial learning, and that landmark sequence knowledge is a feature of spatial knowledge which is affected by navigation aids.

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

AbstractThis study introduces an advanced autonomous navigation algorithm for unmanned surface vehicle (USV) operations in complex coastal traffic areas. To facilitate the undertaking of tests for innovative technologies, the Korean government has designated regulation‐free special zones, thereby enabling field tests for USV demonstrations without an onboard safety person on real manned‐ship navigation routes. To enable real‐world USV operation without onboard safety person, an existing autonomous navigation algorithm is extended to achieve sufficiently reliable and robust USV operation. Accordingly, berthing and unberthing algorithms are newly developed for unmanned operation, and enhanced situational awareness algorithms are applied to detect all types of obstacles, including small‐sized floating objects spread over a wide range, such as a fish farm. In particular, the Korean intelligent maritime transportation service, called e‐Navigation, is incorporated into the autonomous navigation algorithms to overcome the limitations of vehicle autonomy based on onboard sensors. To demonstrate the capability of fully unmanned operation in real marine traffic scenarios, long‐distance navigation (54 km on a single voyage) was conducted by running several missions, including navigation in complex fish farm areas, maritime surveys, collision avoidance, navigation on real traffic routes, and emergency response. The extended navigation algorithms and results of the field tests are presented and discussed in this paper.

A Lightweight Privacy-preserving Path Selection Scheme in VANETs

<p>With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure and efficient road navigation while protecting privacy is a crucial issue. In this paper, we propose a privacy-preserving path selection protocol that supports a token as the object in the oblivious transfers, which effectively reduces the communication overhead. In addition, a lightweight dual authentication and group key negotiation protocol is provided to support dynamic joining or leaving of group members. Moreover, it can guarantee the security of forward data. After experimental analysis, the proposed protocol has high security and efficiency.</p> <p> </p>

Assessment and Comparison of Microplastic Contamination in Atlantic Navigation Routes with Known Uncertainty.

Microplastics (MP) have been found in various environments worldwide. However, not many studies focus on the open ocean due to logistical restraints. Between January and May 2020, the NRP Sagres sampled 123 linear paths of subsurface water of the Atlantic Ocean, passing by Cape Verde, the east coast of South America, and the west coast of Africa. The water was sampled through the ship's water system. The membranes were analyzed by the Hydrographic Institute of Portugal and the Norwegian Institute for Water Research by micro-FTIR. The contamination levels were reported with uncertainty, for 99% confidence level (CL), normalized for filtered water volume and the distance traveled during sampling. Uncertainties were calculated through a detailed ″bottom-up″ evaluation. MP were found in about a third of the stations (48 out of 123), and most of those stations (43 out of 48) presented concentrations below 1 m-3 km-1. The sites where higher concentrations were registered were the port of the island of Santiago (Cape Verde) ((5.9 ± 5.2) m-3 km-1), the Guanabara Bay in Rio de Janeiro (Brazil) ((41 ± 27) m-3 km-1), and close to South Africa ((4.9 ± 2.4) m-3 km-1). Most MP found were polyamide, polyester, polyethylene, ethylene vinyl acetate, and poly(methyl methacrylate). The estimated contamination levels cannot be directly compared with information obtained in other studies due to differences in how MP were determined and the unknown uncertainty of their measured values. This article presents a relevant and reliable contribution to understanding the MP distribution in the Atlantic Ocean.

Arctic Route Planning and Navigation Strategy: The Perspective of Ship Fuel Costs and Carbon Emissions

Arctic navigation research will be more urgent as it is predicted that 4.7% of the shipping trade will be deployed in the Arctic region by 2030. This paper presents a multi-objective path optimization algorithm based on the field theory to investigate Arctic shipping route design and navigation strategies that take safety, economy and environmental protection as factors. The algorithm considers the ice conditions in Arctic waters throughout the year, the energy consumption of ships with different ice classes and carbon emissions. The sea environment is initially modeled using the potential field method, incorporating the ice condition field, fuel consumption, emission field and directional field. The navigable area is defined based on the ship navigability, and path optimization is conducted using the principle of gradient descent between the departure point and destination. The experimental results show that the cost of carbon emissions and fuel consumption varies for each class of ship on the planned path in different months. Therefore, by combining different classes of ships and months, we obtain an Arctic navigation strategy with a consideration of the best cost and environmental protection for each month. The path planning and navigation strategy decision method proposed in this paper can provide a reference for an Arctic route design, navigation month and vessel selection, which is beneficial to the sustainable development of Arctic shipping.

Predictive hierarchical reinforcement learning for path-efficient mapless navigation with moving target

Deep reinforcement learning (DRL) has been proven as a powerful approach for robot navigation over the past few years. DRL-based navigation does not require the pre-construction of a map, instead, high-performance navigation skills can be learned from trial-and-error experiences. However, recent DRL-based approaches mostly focus on a fixed navigation target. It is noted that when navigating to a moving target without maps, the performance of the standard RL structure drops dramatically on both the success rate and path efficiency. To address the mapless navigation problem with moving target, the predictive hierarchical DRL (pH-DRL) framework is proposed by integrating the long-term trajectory prediction to provide a cost-effective solution. In the proposed framework, the lower-level policy of the RL agent learns robot control actions to a specified goal, and the higher-level policy learns to make long-range planning of shorter navigation routes by sufficiently exploiting the predicted trajectories. By means of making decisions over two level of policies, the pH-DRL framework is robust to the unavoidable errors in long-term predictions. With the application of deep deterministic policy gradient (DDPG) for policy optimization, the pH-DDPG algorithm is developed based on the pH-DRL structure. Finally, through comparative experiments on the Gazebo simulator with several variants of the DDPG algorithm, the results demonstrate that the pH-DDPG outperforms other algorithms and achieves a high success rate and efficiency even though the target moves fast and randomly.

Open HD map service model: an interoperable high-Definition map data model for autonomous driving

ABSTRACT With the growth in the vehicle industry, autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors. Maps, as pivotal geospatial information carriers, play a vital role in route planning and navigation service. Compared with conventional maps, high-definition (HD) maps possesses higher precision, richer information, and various services and are regarded as critical infrastructure for autonomous driving. However, heterogeneous HD map data standards and models have different characteristics and advantages, and thus they rarely meet all autonomous driving requirements for different driving objectives. This research presents an interoperable map data model, the Open HD Map Service Model (OHDMSM), to provide a reference for HD map development. The designed OHDMSM, which contains three data layers and a set of corresponding interfaces, demonstrates high interoperability for HD map data fusion and application. As a proof of concept, an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM. The design and development of OHDMSM data structures, interfaces and systems will benefit data requesting, updating, and interoperation for HD map data worldwide, which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.

Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network.

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.

A Hardware-in-the-Loop Simulator to Optimize Autonomous Sailboat Performance in Real Ocean Conditions

In this work, a hardware-in-the-loop (HIL) simulator is designed to diagnose the behavior of an autonomous sailboat as it navigates between waypoints. At its core, the HIL simulator includes the sailboat pilot on an embedded system. The sensor data input to the embedded system is fed by a navigation simulator that takes into account the different forces on the sailboat due to the wind, waves and current conditions. The HIL simulator is then tested for a navigation route from sea trials published in 2014, and the behavior of the automated pilot is compared to its behavior when the vessel is driven by a crew. As demonstrated, the automated system can outperform the man-operated vessel. The tool is also used to diagnose weaknesses in the sailboat autopilot algorithm that can be improved in the future.

Bio-Inspired Multi-UAV Path Planning Heuristics: A Review

Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.

Using virtual lines of navigation for a successful transcortical approach.

Neuronavigation systems have become essential tools in image-guided neurosurgery that aid in the accurate resection of brain tumors. Recent advancements to these devices can indicate the precise location of lesions but can also project an augmented reality (AR) image on the microscope eyepiece to facilitate a successful surgical operation. Although the transcortical approach is a very popular method in neurosurgery, it can lead to disorientation and can cause unnecessary brain damage when the distance from the brain surface to the lesion is long. Herein, we report on an actual case in which a virtual line from AR images was used to assist the transcortical approach. A virtual line connecting the entry point and the target point, which were set as the navigation route, was created using Stealth station S7® (Medtronic, Minneapolis, USA). This line was projected as an AR image on the microscope eyepiece. It was possible to reach the target point by proceeding through the white matter along the displayed virtual line. The lesion was reached within a short duration using virtual line without disorientation. Setting a virtual line as an AR image using neuronavigation is a simple and accurate method that can effectively support the conventional transcortical approach.

An energy scheduling method for clustering islands with shared power exchanging vessels

Pelagic clustering islands are rich sources of renewable energy. However, they are isolated by natural sea areas, making it difficult to realize the interconnectivity of energy generation and utilization among different types of clustering islands. This paper proposes the strategy of coordinated and optimal energy scheduling for pelagic clustering islands based on shared power exchanging vessels (SPEV). First, the energy interconnection model among different types of islands is analyzed based on the concept of SPEV sharing. Second, the space-temporal coupling of SPEV navigation is established by considering SPEV sharing transportation capacity and the charging-discharging state of mobile battery packs. On this basis, a clustering islands energy scheduling model is established to minimize the comprehensive operation costs of clustering islands, considering the battery pack charge–discharge constraints, SPEV navigation route constraints, and passenger traveling constraints. The established energy scheduling model fully explores the space-temporal energy transfer flexibility of SPEV considering meeting the travel demands of passengers. Finally, clustering islands in the South China Sea are considered in the case study, and the simulation results indicate that the proposed SPEV-based clustering islands energy scheduling strategy can effectively improve the renewable energy consumption rate and the operating economy of clustering islands.

RNGDet++: Road Network Graph Detection by Transformer With Instance Segmentation and Multi-Scale Features Enhancement

The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are usually annotated by human experts manually, which is time-consuming and labor-intensive. To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded. Most existing methods either adopt a post-processing step on semantic segmentation maps to produce road network graphs, or propose graph-based algorithms to directly predict the graphs. However, these works suffer from hard-coded algorithms and inferior performance. To enhance the previous state-of-the-art (SOTA) method RNGDet, we add an instance segmentation head to better supervise the training, and enable the network to leverage multi-scale features of the backbone. Since the new proposed approach is improved from RNGDet, we name it RNGDet++. Experimental results show that our RNGDet++ outperforms baseline methods in terms of almost all evaluation metrics on two large-scale public datasets. Our code and supplementary materials are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tonyxuqaq.github.io/projects/RNGDetPlusPlus/</uri> .

Rewarded Maze Training Increases Approach Behavior in Rats Through Neurogenesis-Dependent Growth of Ventral Hippocampus–Prelimbic Circuits

BackgroundLearning complex navigation routes increases hippocampal volume in humans, but it is not clear whether this growth impacts behaviors outside the learning situation or what cellular mechanisms are involved. MethodsWe trained rats with pharmacogenetic suppression of adult neurogenesis and littermate controls in 3 mazes over 3 weeks and tested novelty approach behavior several days after maze exposure. We then measured hippocampus and prelimbic cortex volumes using magnetic resonance imaging and assessed neuronal and astrocyte morphology. Finally, we investigated the activation and behavioral role of the ventral CA1 (vCA1)-to-prelimbic pathway using immediate-early genes and DREADDs (designer receptors exclusively activated by designer drugs). ResultsMaze training led to volume increase of both the vCA1 region of the hippocampus and the prelimbic region of the neocortex compared with rats that followed fixed paths. Growth was also apparent in individual neurons and astrocytes in these 2 regions, and behavioral testing showed increased novelty approach in maze-trained rats in 2 different tests. Suppressing adult neurogenesis prevented the effects on structure and approach behavior after maze training without affecting maze learning itself. The vCA1 neurons projecting to the prelimbic area were more activated by novelty in maze-trained animals, and suppression of this pathway decreased approach behavior. ConclusionsRewarded navigational learning experiences induce volumetric and morphologic growth in the vCA1 and prelimbic cortex and enhance activation of the circuit connecting these 2 regions. Both the structural and behavioral effects of maze training require ongoing adult neurogenesis, suggesting a role for new neurons in experience-driven increases in novelty exploration.