Route Navigation Research Articles

Hybrid path planning method for USV using bidirectional A* and improved DWA considering the manoeuvrability and COLREGs

This paper proposes a hybrid dynamic path planning algorithm that integrates global path planning (GPP) and local path planning (LPP). The objective is to effectively address the challenges associated with path planning and collision avoidance for unmanned surface vehicles (USV) in complex environments. The bidirectional A* algorithm is used to search the optimized strategy based on the actual geographical information map for the navigation routes of USV. The Dynamic Window Algorithm (DWA) is employed, and a novel cost function is formulated considering the USV's manoeuvring characteristics, dynamic constraints, and environmental information. The primary purpose is to ensure the compliance of the USV with COLREGs during dynamic obstacle avoidance. It accomplishes this by providing positive reinforcement for achieving the correct turning speed when the USV determines the orientation of an approaching ship. Another evaluation function is introduced to mitigate the problem of local optima in complex environments that the LPP may encounter. The actual work map is used to model the application scenario with the obstacles in the presence of incoming ships, the simulation results show that the proposed algorithm is capable of generating adaptive, collision-free routes while adhering to COLREGs rules.

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism.

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

Evincing the social and economic impact of inland waterways transportation in the development of the Port Harcourt metropolis

Road traffic congestion has been decreased by using water transportation. The impact of inland waterways transportation on the socio-economic growth of Port Harcourt Metropolis was investigated in this essay. The study adopted a quantitative research approach using a cross-sectional survey design and employed a multi-stage sampling technique. The first stage involves identifying the locations of the jetties and navigable routes available in the metropolis. The second stage involves the listing of the jetties and navigable routes found within the study area which nine (9) jetties and navigable routes were identified. The third stage involves purposively selecting six (6) jetties out of the nine (9) jetties listed. The selected jetties and navigable routes include (Bonny/Bille/Nembe, Marine Base, Abonnema Wharf, Maccoba, Iwofe, and Akpos). The six (6) jetties were selected because of their frequent usage by commuters. The fourth stage involves randomly selecting 200 commuters who are using these jetties and navigable routes for sampling. Using a random sample technique, a structured questionnaire was designed and distributed to the commuters to acquire data for the study. Descriptive statistics (tables and charts) and inferential statistics were used to analyse the data set. Other sources of data for the study include direct observation and photographs to characterise the study area and inland waterways transportation development in the metropolis. The reason the study chose to focus on the commuters and boat operators is because the commuters and operators are individuals who commonly use inland waterways transportation systems. The study found that lack of funds, poor river basin dredging and canalisation, insufficient security and infrastructure, a lack of safety devices, and inadequate training of boat operators and drivers are among the issues and obstacles. Therefore, the study recommends that the government should support efforts to enhance boat service operations for sustainable modes of transportation to promote their use by more people and ease traffic on the roads.

Ecological connectivity of invasive and native fishes in a historic navigation waterway

Regulated waterways, interconnected by navigation barriers (locks and dams), are uniquely difficult to manage, given interest in enabling native species connectivity while minimizing invasions. Canada's historic Rideau Canal Waterway, a 202 km navigable route located in eastern Ontario and connected by 24 lockstations, embodies this challenge. The lock(s) and water-control dam that compose each lockstation may respectively offer a connectivity pathway, though to what extent is unclear. We used acoustic telemetry (native largemouth bass ( Micropterus nigricans) and northern pike ( Esox lucius), and invasive common carp ( Cyprinus carpio); n = 224) to evaluate fish connectivity relative to lock operations and environmental data over three years (2019–2021). Thirty-five passages by 23 native fishes were recorded, with 49% of passages through locks. No common carp passages were detected; movements indicate that they favour higher flow areas downstream of dams, regions with no pathway upstream. Most passages were downstream and, of concern to obligate upstream migrators, we found that multi-flight and higher-lift locks appear impassable to upstream movements. Our results suggest that these lockstations limit, but not entirely restrict, connectivity.

LSTM-Aided Selective Beam Tracking in Multi-Cell Scenario for mmWave Wireless Systems

Millimeter wave systems rely on narrow beams (beamforming) and dense cell deployments for reliable communication. Tracking these beams from multiple cells can increase power consumption and signaling overhead. Therefore, a mobile needs to selectively and smartly track beams under power/overhead constraints. In this paper, we propose a fully data-driven, long short-term memory (LSTM)-based, selective link tracking approach. These approaches are developed for both fixed and adaptive power/overhead constraints, which also predict the magnitude of the best performing beam. The algorithms are validated in simulations of a 28GHz 5G New Radio (NR)-like system in an urban area with realistic navigation routes utilizing detailed ray-tracing. The simulations demonstrate that the proposed methods outperform classic and deep reinforcement learning (RL) approaches in terms of tracking accuracy, power saving and overhead for both analog and digital beamforming architectures. We also argue that the prediction of the proposed method can be easily performed on a digital signal processor of a modern chipset with minimal resource consumption.

Review of Intelligent Ship Path Planning Algorithms

Intelligent ship route planning has become an important research direction in the field of shipping in recent years. This paper first provides an overview of the basic theory of intelligent ship route planning. Secondly, various intelligent ship route planning algorithms are introduced, including methods based on A* algorithm, artificial potential field algorithm, RRT algorithm, and reinforcement learning. These algorithms analyze information such as ocean environment, predict sea conditions and traffic conditions, and consider ship dynamics and navigation safety constraints to provide efficient and safe navigation routes for ships. Finally, this paper points out the key issues and future development directions in intelligent ship route planning. The continuous innovation and application of intelligent ship route planning algorithms will provide more intelligent and efficient ship transportation services for the shipping industry, promoting the sustainable development of the shipping industry.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists.

Average Displacement of Multi-Beam Measurement Vessel based on 2D Particle Swarm Algorithm

To address the optimization of a survey vessel's navigation route, this study uses historical sea depth data from a maritime area several years ago as an example. The research employs the two-dimensional Particle Swarm Optimization (2DPSO) algorithm to calculate the direction and distance for vessel navigation while adhering to constraints, aiming to obtain the optimal route for the survey vessel. Traditional Particle Swarm Optimization exhibits rapid early-stage convergence, but slows in the later stages and is susceptible to local optima. Hence, we opt for the 2DPSO algorithm, known for its simpler structure and incorporation of penalty functions to constrain particle values. This enhances algorithm performance and minimizes the risk of falling into local optima, offering a better solution to optimal route planning.

Sea Ice Examination for Ice Class Ships visiting Horseshoe Island, Antarctica, Using MODIS in 2000-2022

Sea ice has a significant impact on numerous areas, ranging from the reflection of the sun rays back on the albedo scale, to spurring intense phytoplankton blooms that help maintain the marine ecosystem and making the navigation of some maritime routes challenging. The formation of sea ice in the Southern Ocean affects maritime logistics and scientific operations on Antarctica, especially on the Antarctic Peninsula, which hosts half of the scientific stations. Ships needed to supply British and Turkish scientific bases on Horseshoe Island in the southernmost part of the Antarctic Peninsula are in high demand. Therefore, the analysis of the oldest trends of sea ice that affect maritime operations is crucial. The goal of the study was to examine the formation of sea ice, and especially ice-free days, by using images collected between 2000 and 2022 by over 8000 NASA’s Terra/MODIS Moderate Resolution Imaging Spectroradiometer instruments (MODIS). An analysis of maritime traffic gives a better understanding of maritime operation needs and usage of natural ports, including bays within the research area. Therefore, the study focused on four sites: western approach to Horseshoe Island, Sally Cove (hosting a British station), Lystad Bay (hosting a Turkish station) and Gaul Cove. The results indicate that there has been no multiyear sea ice around Horseshoe Island. The most suitable months for the safe navigation of any type of ship in the area, due to the greatest number of ice-free days, are March, February, April and January. The study reveals that, in accordance with the Polar Code, lower ice class ships are required for safe navigation during scientific expeditions to Horseshoe Island. Also, as the average number of days with clear skies was 38, and average cloudiness very high, the use of optical satellites for long-term monitoring studies on Horseshoe Island is not recommended.

A comprehensive energy analysis of a hybrid motorization for small/medium boats

Small/Medium boats are considered as favourite maritime transportation, and they are mostly propulsed by Outboard Motors. Boats are designed for recreative, fishing and surveillance purposes, however in tropical undeveloped countries boats are employed for different applications such as passenger and goods transportation fairly away from nominal power engine, which lies from 8 to 12% engine efficiency. The present survey aims to develop a novel navigation perspective in a power and efficiency analysis of Plug-in Hybrid Electric Ship (PHES) 1. An internal combustion engine (ICE) is converted in hybrid propulsion system (HPS) coupling an electric motor powered by batteries through an electric DC bus device in a parallel configuration system, in order to increasing the traveled distance to fuel consumption ratio. Parameters such as boat size, engine hybrid configuration, were simulated on a schematic model system following an actual navigation route drawn, improving a 25-30% overall system efficiency respect nominal engine performance.

Research on the Route Choice Behavior of Urban Freight Vehicles Based on GPS Data

In order to provide urban freight vehicles with navigation routes that better align with their travel preferences, it is necessary to analyze the patterns and characteristics of their route choices. This paper focuses on freight vehicles traveling within the city and examines their route selection behavior. Through an analysis of the GPS data collected from freight truck journeys in Changchun, China, this study outlines the characteristics of freight vehicle travel within the city. Variables that may influence their route selection behavior are defined as feature factors. The study employs the DBSCAN algorithm to identify the hotspots in origin–destination pairs for freight truck travel in Changchun. It also utilizes Breadth First Search Link Elimination to generate a set of route choices and constructs route selection behavior models based on Multinomial Logit and Path Size Logit. Based on the research findings, during navigation within the city road network, these vehicles exhibit a preference for side roads, a tendency to favor right turns at intersections, and a propensity to choose routes with lower duplication compared to alternative options. The study’s conclusions offer theoretical support for guiding urban freight vehicle routes and planning and managing freight traffic within the city.

Familiarity-taxis: A bilateral approach to view-based snapshot navigation

Many insects use view-based navigation, or snapshot matching, to return to familiar locations, or navigate routes. This relies on egocentric memories being matched to current views of the world. Previous Snapshot navigation algorithms have used full panoramic vision for the comparison of memorised images with query images to establish a measure of familiarity, which leads to a recovery of the original heading direction from when the snapshot was taken. Many aspects of insect sensory systems are lateralised with steering being derived from the comparison of left and right signals like a classic Braitenberg vehicle. Here, we investigate whether view-based route navigation can be implemented using bilateral visual familiarity comparisons. We found that the difference in familiarity between estimates from left and right fields of view can be used as a steering signal to recover the original heading direction. This finding extends across many different sizes of field of view and visual resolutions. In insects, steering computations are implemented in a brain region called the Lateral Accessory Lobe, within the Central Complex. In a simple simulation, we show with an SNN model of the LAL an existence proof of how bilateral visual familiarity could drive a search for a visually defined goal.

Fully Digital Audio Haptic Maps for Individuals with Blindness

Tactile maps designed for individuals with blindness can greatly improve their mobility, safety and access to new locations. While 3D-printed maps have already been demonstrated to be a powerful tool for delivering spatial information, they might not always be available. Alternatively, a combination of audio and haptic information can be used to efficiently encode 2D maps. In this paper, we discuss the development and user-testing of a novel audio-haptic map creator application. Maps created using this application can provide people with blindness with a tool for understanding the navigational routes and layouts of spaces before physically visiting the site. Thirteen people with blindness tested various components of the virtual map application, such as audio, haptic feedback and navigation controls. Participants’ data and feedback were collected and analyzed to determine the effectiveness of the virtual maps as it relates to this user group’s readability and usability. The study showed that it was easy to use and that it efficiently delivered information about travel routes and landmarks that the participants could successfully understand.

Ship Route Planning for Collision Avoidance Based on the Improved Isochrone Method

If ship navigators could access precise collision risk data and guidance for avoidance, safer navigation would be feasible. This study introduces a method for devising avoidance routes based on quantitative collision risk assessment. In particular, we propose an improved isochrone method, a cell-free approach akin to real ship navigation routes. The method uses a novel heuristic function to ensure effective collision avoidance, even in scenarios where conventional heuristic algorithms fail. Through verification cases, we demonstrate that the proposed method successfully avoided collisions, even in situations with unexpected lined-up ships, reducing maximum collision risk by up to 94.0%. Applications of the proposed method with actual AIS (Automatic Identification System) data yield simulation results with successful collision avoidance route planning. Comparing the proposed method to avoidance routes using other existing methods and to the actual ship’s route, the proposed method was able to plan a route with a 37.5% improvement in maximum collision risk and a 13.6% improvement in average collision risk.

Crime-Avoiding Routing Navigation

Extensive prior work has provided methods for the optimization of routing based on the criteria of travel time and/or the cost of travel and/or the distance traveled. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph to find the best route. Some users desire that the routing suggestion include consideration pertaining to the reduction of risk of encountering violent crime. For example, a user desires a leisurely walk via a safe route from her hotel in an unknown city. Here, we present a method to quantify such user preferences and the risks of encountering crime and to augment the standard routing methods by assigning weights to safety considerations. The proposed method’s advantages, in comparison to other crimeavoidance routing algorithms, include weighting crime types with respect to their potential detrimental value to the user, with temporal qualification and quantification of crime and its statistical aggregation at the geographic resolution down to a city block.

Polar climate change: a multidisciplinary assessment

The rapid environmental changes in polar regions have been attracting considerable political, public, and scientific attention in recent years. The polar amplification is recognized as a robust feature of the climate system in response to carbon dioxide (CO2) forcing, resulting in sea ice loss, ice sheet melting, and methane release from permafrost thawing. From a physical perspective, this paper examines the polar amplification and sea ice changes for past and future scenarios using satellite, reanalysis, and climate model datasets. From an interdisciplinary perspective, we discuss the potential environmental, socioeconomic, and political effects associated with these changes. The observational data showed enhanced warming and rapid changes in sea ice cover in polar regions. Under the largest future CO2 forcing, climate simulations indicate an unprecedented rise in air temperature and fast sea ice loss, even in low emission scenarios. This results in a number of physical, environmental, and social-economic effects that need to be carefully considered. Polar climate change, however, offers new opportunities, including the local increase in fisheries and the opening of new navigation routes, which substantially impact the world economy. At the same time, it also implies critical environmental consequences associated with many socioeconomic and ecological risks, such as migration or extinction of populations and species; sea level rise; an increase in frequency and intensity of extreme weather in mid-latitudes; and infrastructure damage from permafrost thawing. Even with the advances and improvements in climate modeling in recent decades, the exact nature of these nonlinear interactions is still in debate.

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

New Challenges on the Maritime Boundaries Delimitation Dispute Between Greece and Albania in the Corfu Channel

The Albanian-Greek maritime border in the Straits of Corfu region and the Ionian Sea reflects a complicated geographical, historical, and political reality, which constitutes a special characteristic of the Balkan Peninsula coastal region as a whole. For many centuries, the navigational routes of the Strait of Otranto and the Corfu Channel were characterized as important strategic, military, and commercial seaways for the ancient peoples of the region. In this respect, even though the Albanian-Greek southern border region has been at the center of conflicts and disagreements between these countries for many years, the problem is not thought to be completely related to the specific delimitation of the maritime borderline. The interstate maritime boundary is official and internationally recognized and is shown on all official political maps as an international borderline. The Albanian-Greek borderline is a product that came as a result of the Balkan Wars and the First World War, in which there was a great involvement and influence of the Great Powers’ diplomacy. Nowadays, the possibility of Albania-Greece interstate conflicts over the southern border area and maritime borders exists. In this context, ethnographic complexity is considered problematic due to the existence of the Greek minority in Albania, as well as Albanian immigrants living in Greece. On the other hand, the natural resources that possess the maritime regions of the Corfu Channel and the Ionian Sea are another reason for the dispute over the maritime border. However, according to US security institutions, these cross-border disputes between these states may only remain at the political level and not degrade further.

Self-Localization Using Trajectory Attractors in Outdoor Environments

Self-localization in probabilistic robotics requires detailed, geographically consistent environmental maps, which increases the computational cost. In this study, we propose a simple self-localization method that does not require such maps. In the proposed method, the order structure, such as the mobile robot’s navigation route, is embedded as trajectory attractors in the state space of a nonmonotone neural network, and self-position estimation is performed by processing based on the autonomous dynamics of the network. From experiments, we demonstrated the basic performance of the proposed method, including robust self-localization in complex outdoor environments. Furthermore, self-localization is possible on multiple courses with overlapping paths by suitably varying the network dynamics based on environmental information. While issues remain, this study points to the great potential of neurodynamics-based robotic self-localization.

Digital technologies of transportation-related communication: Review and the state-of-the-art

Information and communication technologies (ICTs) play a significant role in transportation systems as they provide the connection between transportation system users, system planners, operators, and managers. Over the last few years, with advancements in ICTs, there has been a rapid change in travel behaviour. However, little is known about how ICTs and other emerging digital technologies are being utilised in transportation-related communication for public, private, and active transportation. To bridge this gap, this study aims to explore academic and grey literature focused on the use of digital technologies in transportation-related communication. The methodological approach employs a systematic literature review, with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) protocol, to identify digital technologies that are found promising among scholars and practitioners for research, testing and deployment. The study analysed 158 publications and identified some common themes of how these digital technologies can be implemented to improve access to transportation communication. The study finds that: (a) The main literature focus is how information is gathered and shared with transportation users; (b) Advances in mobile applications afford real-time route updates, easy navigation of complex routes, and personalised journey planning; (c) Some notable knowledge gaps exist in how these technologies are implemented—including, willingness to use and digital literacy to understand them; (d) Many digital technologies are at the trial or development phases, which have potential to improve how transportation information can be made readily accessible to all users.