Navigation Scenarios Research Articles

Energetic cost of microswimmer navigation: The role of body shape

We study the energetic efficiency of navigating microswimmers by explicitly taking into account the geometry of their body. We show that, whereas arguments based solely on propulsion efficiency lead one to conclude that needle-like swimmers are most energetically efficient, disk-like swimmers rotated by flow gradients naturally follow time-optimal trajectories. The coupling between body geometry and hydrodynamics thus leads to a generic trade-off between the energetic costs associated with propulsion and navigation, which is accompanied by the selection of a finite optimal aspect ratio. We derive from optimal control theory the steering policy ensuring overall minimum energy dissipation, and characterize how navigation performances vary with the swimmer shape. Our results highlight the important role of the swimmer geometry in realistic navigation scenarios. Published by the American Physical Society 2024

On the data-driven investigation of factors affecting the need for icebreaker assistance in ice-covered waters

Merchant vessels often require icebreaker (IB) assistance to create safe pathways and improve efficiency when navigating in the Baltic Sea. Since IB resources are limited, an accurate estimation on the need for IB assistance is important. Whether IB assistance is needed depends on multiple factors. While practical experience from captains is naturally a source of valuable information for the decision on the need for IB assistance, systematic analysis of the reasoning is limited. The primary aim of this paper is to holistically investigate the influencing factors and their effect on estimating the need for IB assistance through data-driven techniques. Based on a comprehensive list of potential factors, different of data such as traffic history, environmental conditions, and ship specifications are gathered to present complex navigational scenarios. Each scenario is labeled by different navigation modes (independent navigation or IB assistance), laying the foundation for influencing factor identification and effect quantification. Logistic regression is applied to evaluate the effect of the factors on the need for IB assistance. The results show that the impact of the factors is diverse, and ridged ice concentration has the most significant impact. The effectiveness of identified factors is measured by comparing it to that of the factors that have been implemented by the existing studies (e.g., the combination of ice concentration, thickness, and ship ice class, or only ship speed). By considering the factors in this study, the classification performance can be improved by at least 5.6%. The findings in this paper can provide insights for predicting IB workloads and optimizing IB resources and have the potential to support the development of an intelligent decision-support system for winter navigation.

Cognitive task analysis-driven intelligent steering wheel interaction design

With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel’s HMI interaction is based on an analysis of users’ affective needs and rational physiological characteristics. This paper integrates the analysis of users’ affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI’s detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI.

An energy-based pulsar period estimation method using Hilbert curve and double CNNs

In order to accurately estimate the period of X-ray pulsars to improve navigation performance, this paper proposes a pulsar period estimation method based on photon energy using reverse Hilbert coding and double Convolutional Neural Networks (CNNs) discrimination. This method uses photon energy information to perform epoch folding. It converts one-dimensional contour information into two-dimensional image signals through reverse Hilbert curve and recognizes contour image features through CNN models to determine the optimal period. According to the simulation results, the period estimation accuracy of this method is 0.5350 ns, which is 65.71% higher than the χ2 epoch folding method. The contour phase accuracy is 0.0004767, which is 41.93% higher than the χ2 epoch folding method. At the same time, the observation time and noise interference that may affect the accuracy of period estimation are also quantitatively analyzed. In addition, in order to simulate real navigation scenarios, this paper also conducts dynamic period estimation experiments on PSR B0531+21 pulsar in a specific period of time. The method proposed in this paper has the advantages of high precision, fast calculation speed, strong anti-interference ability and accurate dynamic period estimation, which can significantly improve the navigation performance of X-ray pulsars.

S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection

The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception.

Research on autonomous mobile robot maze navigation problem based on Dijkstras algorithm

In recent years, the field of autonomous mobile robotics has garnered significant attention due to its potential applications in various domains such as logistics, surveillance, and search and rescue operations. A crucial challenge in this area is the efficient navigation of robots within complex and dynamic environments, particularly when navigating through maze-like structures. The maze navigation problem involves finding optimal paths for robots to traverse from their initial positions to designated destinations while avoiding obstacles and making intelligent decisions to ensure timely and safe navigation. This study aims to investigate and apply Dijkstras algorithm to solve the maze navigation problem for autonomous mobile robots. By analyzing the navigation challenges faced by autonomous mobile robots in maze environments, a solution based on Dijkstras algorithm is proposed. In conclusion, this study contributes to the field of autonomous mobile robotics by proposing and evaluating the application of Dijkstras algorithm for maze navigation. The experimental results validate its potential to address the challenges of navigating intricate maze environments. However, it is acknowledged that further refinement and innovation are possible to continue improving the performance of autonomous mobile robots in maze navigation scenarios.

A water track laser Doppler velocimeter for use in underwater navigation

Doppler velocity log (DVL) is usually employed to suppress the divergency of the Strapdown Inertial Navigation System (SINS) in underwater navigation, which is not concealable due to high transmittance for acoustic wave in the water. To conduct underwater navigation task with high concealment, a differential laser Doppler velocimeter (LDV) working at water track mode is integrated with SINS in this paper. The developed LDV measures the advance velocity of the underwater carrier with respect to the surrounding water in underwater navigation scenario with advantages of high concealment, high real-time performance, high update rate, light weight, and small dimension. A dynamic river test was conducted to validate the underwater navigation performance of SINS/LDV integrated system. The experimental results show that during the voyage of 4493s and 5271.8 m, the maximum horizontal positioning errors of the proposed SINS/LDV integrated underwater navigation system is 27.8 m and the relative position error is less than 0.6% with respect to total distance. Therefore, the water track LDV is practical to aid SINS in underwater navigation environment.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Adaptive federated filter–combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships

The integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship’s motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems.

Event-triggered robot self-assessment to aid in autonomy adjustment.

Introduction: Human-robot teams are being called upon to accomplish increasingly complex tasks. During execution, the robot may operate at different levels of autonomy (LOAs), ranging from full robotic autonomy to full human control. For any number of reasons, such as changes in the robot's surroundings due to the complexities of operating in dynamic and uncertain environments, degradation and damage to the robot platform, or changes in tasking, adjusting the LOA during operations may be necessary to achieve desired mission outcomes. Thus, a critical challenge is understanding when and how the autonomy should be adjusted. Methods: We frame this problem with respect to the robot's capabilities and limitations, known as robot competency. With this framing, a robot could be granted a level of autonomy in line with its ability to operate with a high degree of competence. First, we propose a Model Quality Assessment metric, which indicates how (un)expected an autonomous robot's observations are compared to its model predictions. Next, we present an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm that uses changes in the Model Quality Assessment above a threshold to selectively execute and report a high-level assessment of the robot's competency. We validated the Model Quality Assessment metric and the ET-GOA algorithm in both simulated and live robot navigation scenarios. Results: Our experiments found that the Model Quality Assessment was able to respond to unexpected observations. Additionally, our validation of the full ET-GOA algorithm explored how the computational cost and accuracy of the algorithm was impacted across several Model Quality triggering thresholds and with differing amounts of state perturbations. Discussion: Our experimental results combined with a human-in-the-loop demonstration show that Event-Triggered Generalized Outcome Assessment algorithm can facilitate informed autonomy-adjustment decisions based on a robot's task competency.

Good Seamanship Score Quantification in Complex and Congested Waterways

This paper presents a novel method to quantify seafarers’ good seamanship during navigation scenarios with multi-vessel encounters, in open and confined waters, and to compute COLREG's-compliant path deviations for avoiding collision and grounding. Accurate quantification of good seamanship necessitates comprehensive information about the vessels state and the surrounding environment, obtainable through the AIS system and electronic nautical charts. Our work advances the current state-of-the-art by: (1) incorporating a normalized grounding risk for navigating confined waters, (2) factoring in speed changes for a probabilistic assessment of collision risk, and (3) considering the minimum possible scenario risk based on vessel kinodynamic constraints. The proposed method is evaluated using historical AIS data and sea charts of Danish waters. The evaluation results demonstrate improved fairness scoring by acknowledging that navigators may lack risk-free options.

The effects of Augmented Reality on operator Situation Awareness and Head-Down Time

A lack of navigator's Situation Awareness (SA) is one of the leading causes of maritime accidents. Visually observing the area surrounding a vessel continues to be a critical aspect and best practice of safe navigation to establish and maintain SA. Augmented Reality (AR) allows the placement of information in a user's field of view, which can encourage navigators to spend more time looking up at their external environment whilst still having access to operational data. However, empirical evidence on the impact of AR on maritime operations is limited. This paper investigates the effects of AR on navigator SA & Head-Down Time (HDT) using a within-group quasi-experimental design. Seventeen licensed navigators and nautical students analysed twelve navigation scenarios: six non-AR (control) and six AR (experimental) scenarios using a maritime training simulator. SA was measured via SAGAT scores for each scenario and the SA-SWORD to compare preferences. Each scenario was video recorded and analysed for participant's total amount of HDT and head-down occurrences in each scenario. Results found that the addition of AR significantly reduced participant HDT (by a factor of 2.67) and head-down occurrences (by 62%) in comparison to navigation scenarios without AR. Furthermore, AR did not significantly improve mean SA. This study contributes to the limited empirical data on the effects of AR on operator performance, demonstrating the potential value of AR for improving SA and facilitating increased head-up time during maritime navigation, which in turn could improve safety at sea.

A muon high-resolution pseudorange measurement method: Application to muon navigation in confined spaces

Confined spaces such as polar regions, deep earth and deep ocean are crucial navigation scenarios where traditional navigation techniques have difficulty in obtaining external signals for positioning. The cosmic ray muons, which carry the spatial and energetic information, are easy to penetrate these confined spaces. Therefore, the unique muon characteristic provides a new perspective to estimate detector position, which can be considered using in confined spaces navigation. In this paper, a well-developed theory of muon navigation is established by combining a muon pseudorange measurement method. Moreover, an Equivalent Velocity Calculation Model (EVCM) and a Muon Sequence Matching Technology (MSMT) are proposed. The first model corrects flight pseudorange error caused by the relativistic energy loss and the second technology compensates the random error in pseudorange measurement. Further, a series of simulations are performed to analyze the muon events number which can be received by detector in different scenarios with the variations of zenith angle, detector area, varied detector plates gap, and muon flight distance. Meanwhile, the simulation results demonstrate that the muon navigation update rate every 10 minutes can reach 5.989 in confined spaces at 100 m, and further pseudorange error analysis indicates that the meter-level positioning accuracy can be acquired. Finally, we construct a muon coincidence measurement scheme and verify that the laws of the muon positioning system for high-energy muons are consistent with the simulation results.

ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING

Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.

EAT: Environment Agnostic Traversability for reactive navigation

This work presents EAT (Environment Agnostic Traversability for Reactive Navigation) a novel framework for traversability estimation in indoor, outdoor, subterranean (SubT) and other unstructured environments. The architecture provides updates on traversable regions online during the mission, adapts to varying environments, while being robust to noisy semantic image segmentation. The proposed framework considers terrain prioritization based on a novel decay exponential function to fuse the semantic information and geometric features extracted from RGB-D images to obtain the traversability of the scene. Moreover, EAT introduces an obstacle inflation mechanism on the traversability image, based on mean-window weighting module, allowing to adapt the proximity to untraversable regions. The overall architecture uses two LRASPP MobileNet V3 large Convolutional Neural Networks (CNN) for semantic segmentation over RGB images, where the first one classifies the terrain types and the second one classifies see-through obstacles in the scene. Additionally, the geometric features profile the underlying surface properties of the local scene, extracting normals from depth images. The proposed scheme was integrated with a control architecture in reactive navigation scenarios and was experimentally validated in indoor and outdoor environments as well as in subterranean environments with a Pioneer 3AT mobile robot.

Effect of Speed and Hull Length on the Hydrodynamic Performance of a Semi-Planing Hull of a Shallow-Draft Watercraft

Hydrodynamic performance is an essential factor in the design of a watercraft, and the navigation scenario determines the complexity of its operation. This study aims to identify the effect of speed and length on the hydrodynamic behavior of a semi-planing watercraft in shallow waters. A computational fluid dynamics tool was employed to predict the trim, heave, and resistance parameters of two different hulls: a base hull and a craft with an increased hull length. The two hulls had similar hydrodynamic characteristics. The effects of speed and hull length on these predicted parameters obtained for the two hulls were compared. The results showed a low resistance uncertainty and a reduction in dynamic trim for longer hull lengths. These findings highlight the importance of considering balance and dynamic trim in designing shallow-draft watercrafts to ensure an optimal performance in specific conditions, such as rivers with depth restrictions.

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness.

Intelligent Systems in Motion

Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

GoComfort: Comfortable Navigation for Autonomous Vehicles Leveraging High-Precision Road Damage Crowdsensing

Recent years have witnessed rapid advances in autonomous driving technologies. Autonomous vehicles are more likely to be accepted if they drive comfortably to avoid potholes, bumps, and other road damage conditions, especially when there are elderly and disabled passengers on board. Traditionally, sensing road damage conditions is either labor-intensive by field investigation and reporting, or inaccurate by surveillance cameras and driving recorders due to limited perspective. In this paper, we propose GoComfort, a crowdsensing-based framework to provide low-cost and fine-grained comfortable navigation for autonomous vehicles with road damage identification leveraging high-precision road sensing data. First, we propose to exploit city-wide autonomous vehicle fleets as crowdsensing participants, and employ an edge-cloud-hybrid computing paradigm to efficiently collect high-precision road damage-related data, including 3D LiDAR point clouds and street view images. Second, we design an accurate road damage identification model fusing spatial structures of point clouds and texture features of street view images, and use an active learning-based method to address the sparse labels issue. Finally, we devise two comfortable navigation scenarios, i.e., fine-grained road damage avoidance and coarse-grained city-wide navigation, and propose a hierarchical road damage assessment diagram for comfortable route planning. Experiments using real-world road sensing data in Xiamen, China show that our approach identifies road damage conditions with an accuracy of 87.5%, and achieves a user acceptance rate of 93.8% in riding comfort evaluation, outperforming the state-of-the-art baselines.