Free Navigation Research Articles

Traffic advisory for ship encounter situation based on linear dynamic system

Enhancing Situation Awareness (SA) is crucial for maritime traffic safety. Various indicators have been developed to assess risks in encounter situations and support the SA of Vessel Traffic Service Operators (VTSOs) and Officers on Watch (OOW), including collision risk and traffic complexity. Despite the widespread use of these navigational aids, ship collision incidents have not been effectively reduced. This paper abstracts ship encounter situations as linear dynamic systems to enhance the understanding of traffic situations. A traffic advisory framework based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed by integrating complexity metrics with risk indicators. The proposed method is validated through simulations of head-on, overtaking, and crossing scenarios, demonstrating its ability to accurately assess encounter complexity and issue advisories for free navigation, complexity, and resolution. Finally, we discuss the practical application of the proposed method through real-world experiments conducted in the waters of Qiongzhou Strait. The results indicate that the proposed method effectively quantifies the complexity of ship encounter situations and identifies high-collision-risk vessels from a microscopic perspective while providing insights into maritime traffic surveillance from a macro perspective.

Addressing maritime security challenges through maritime domain awareness: a study of the role of the Information Fusion Centre-Indian Ocean Region

ABSTRACT In recent years, many non-traditional maritime safety and security threats viz. piracy, armed robberies at sea, maritime terrorism, human trafficking, drugs trafficking, illicit trafficking in wildlife, illegal deals in weapons, IUU fishing, dilapidation of ocean health and illegal exploitation of sea resources, etc. have emerged as real and serious challenges not only to the commercial shipping but also to the national security. A comprehensive response from the worldwide seafaring community is the need of the hour to tackle these maritime threats, both natural and man-made. India’s initiative of establishing the Information Fusion Centre-Indian Ocean Region (IFC-IOR) in the year 2018 can be regarded as a significant step in dealing with the variety of maritime security challenges. Jointly managed and run by the Indian Navy and Coast Guard, the IFC-IOR fulfils the purpose of creating Maritime Domain Awareness on the issues pertaining to maritime safety and security. Protecting maritime interests and augmenting security in the Indian Ocean Region (IOR) is indispensable for maintaining free navigation and overflight, unconstrained commerce and a free, open and UNCLOS-based regime in the IOR.

A scene with an invisible wall - navigational experience shapes visual scene representation.

Human navigation heavily relies on visual information. Although many previous studies have investigated how navigational information is inferred from visual features of scenes, little is understood about the impact of navigational experience on visual scene representation. In this study, we examined how navigational experience influences both the behavioral and neural responses to a visual scene. During training, participants navigated in the virtual reality (VR) environments which we manipulated navigational experience while holding the visual properties of scenes constant. Half of the environments allowed free navigation (navigable), while the other half featured an 'invisible wall' preventing the participants to continue forward even though the scene was visually navigable (non-navigable). During testing, participants viewed scene images from the VR environment while completing either a behavioral perceptual identification task (Experimentl) or an fMRI scan (Experiment2). Behaviorally, we found that participants judged a scene pair to be significantly more visually different if their prior navigational experience varied, even after accounting for visual similarities between the scene pairs. Neurally, multi-voxel pattern of the parahippocampal place area (PPA) distinguished visual scenes based on prior navigational experience alone. These results suggest that the human visual scene cortex represents information about navigability obtained through prior experience, beyond those computable from the visual properties of the scene. Taken together, these results suggest that scene representation is modulated by prior navigational experience to help us construct a functionally meaningful visual environment.

Bodily and Visual-Cognitive Navigation Aids to Enhance Spatial Recall in Mild Cognitive Impairment.

Individuals with mild cognitive impairment (MCI) syndrome often report navigation difficulties, accompanied by impairments in egocentric and allocentric spatial memory. However, studies have shown that both bodily (e.g., motor commands, proprioception, vestibular information) and visual-cognitive (e.g., maps, directional arrows, attentional markers) cues can support spatial memory in MCI. We aimed to assess navigation cues for innovative spatial training in aging. Fifteen MCI patients were recruited for this study. Their egocentric and allocentric memory recall performances were tested through a navigation task with five different virtual reality (VR) assistive encoding navigation procedures (bodily, vision only, interactive allocentric map, reduced executive load, free navigation without cues). Bodily condition consisted of an immersive VR setup to engage self-motion cues, vision only condition consisted of passive navigation without interaction, in the interactive allocentric map condition patients could use a bird-view map, in the reduced executive load condition directional cues and attentional markers were employed, and during free navigation no aid was implemented. Bodily condition improved spatial memory compared to vision only and free navigation without cues. In addition, the interactive allocentric map was superior to the free navigation without cues. Surprisingly, the reduced executive load was comparable to vison only condition. Moreover, a detrimental impact of free navigation was observed on allocentric memory across testing trials. These findings challenge the notion of an amodal representation of space in aging, suggesting that spatial maps can be influenced by the modality in which the environment was originally encoded.

Indoor functional subspace division from point clouds based on graph neural network

Indoor scenes are closely related to human life and contain rich geometric and semantic information. Dividing indoor spaces from data is crucial for multiple applications, such as navigation and digital twins. However, achieving this task is challenging. Traditional indoor space division methods only represent buildings, floors, and rooms to some extent, lacking semantic descriptions of indoor elements and their spatial arrangements. To divide an indoor space more finely, a novel indoor space subdivision method is proposed in this paper. Our method leverages the flexible space subdivision framework (FSS) to categorize indoor space into free navigation subspace, object subspace, and functional subspace. To define functional subspaces, we present a taxonomy for the spatial layout patterns of common indoor elements (like tables, chairs, ceilings, walls, floors, etc.). Then, we introduce scene graphs to represent indoor 3D scenes, where each node represents an indoor element and each edge encodes the spatial relationship between the elements. Finally, a node classification network is proposed to segment indoor scene into subspaces and predicts their (semantic) functions. We select 9 buildings of Matterport3D and 6 areas in S3DIS and merge them to form our dataset for training and testing our method. Experiments yield good results with up to 90.42% accuracy and 85.28% F1-scores in overall space subdivision. Moreover, compared with the various graph node classification networks, our method has achieved the best performance in indoor space subdivisions.

Viewpoint Free Navigation Comfort and Quality Measurement for Windowed 6DoF Synthesized Videos

Viewpoint Free Navigation Comfort and Quality Measurement for Windowed 6DoF Synthesized Videos

The development of the international regime of free navigation on the Danube

The development of the international regime of free navigation on the Danube

Dynamics of space coding by mouse hippocampal CA1 field neurons in a free navigation task in different environments

The development of persistent cognitive adaptations in neurons is a key area of focus in contemporary neuroscience. A significant illustration of such adaptations occurs through spatial specializations in place cells with one or more receptive fields that are sensitive to spatial cues (place fields) [1]. In prior research, we extensively examined the dynamics of place field formation in granular layer neurons of the CA1 field in the hippocampus of mice during arbitrary free navigation in a circular track [2]. As the primary factor for maintaining stability in the spatial specialization of place cells, we have introduced dynamic selectivity. This approach enables us to monitor the latency of each place field’s formation and the selectivity dynamics of place cells to their respective fields throughout one or multiple shooting sessions. However, in one-dimensional settings, like the circular track referenced previously, place fields can demonstrate direction-specificity with regards to animal movement [3]. This factor considerably complicates the analysis for arbitrary movement trajectories of animals. In this study, additional experiments were conducted to capture the neural activity of mice in two-dimensional environments. The experiments were conducted in both a rectangular field with objects and a circular arena with a varying number of obstacles. In this paper, we conduct a comparative analysis to examine the critical parameters governing the development of spatial specializations in a one-dimensional circle track and two-dimensional arenas. These parameters include specialization latency, average dynamic selectivity, initial increase in selectivity, and the proportion of “immediate” place fields that remain stable from the first animal visit. The study discovered that the mean selectivity of the place fields escalated in each session, with higher rates observed in subsequent sessions versus the initial session in a novel setting. Additionally, there was a significant occurrence of “immediate” place fields, representing 11% of all place fields in the rounded arena with obstacles, and 25% of all place fields in the circular track. Additionally, a population analysis of neural activity was performed on the first session in a circular track and a round arena with obstacles. Using Laplace eigenmaps for the dimension reduction of population vectors, the trajectory of animals was reconstructed, and the accuracy of this reconstruction agreed with the average dynamic selectivity for each animal included in the analysis. Thus, dynamic selectivity was verified as a measure of the spatial coding quality of the entire registered population of neurons.

The EU and the governance of the Maritime Global Space

ABSTRACTThis article investigates the extent to which the European Union (EU) contributes to the governance of Global Spaces by exploring its policies towards the maritime domain. In a more competitive and uncertain geopolitical setting, are the EU’s policies changing and becoming more strategic? Or does the EU continue to promote multilateral cooperation and regulation of the maritime Global Space, and if so, what type of governance regimes does it promote? Developing and applying three analytical models of Global Space policies, the article finds that the EU has been consistent in its approach, which reflects a combination of its strong interest in free navigation and an attempt to achieve sustainable growth through climate regulation. Despite more geopolitical conflict in these areas and in international relations more broadly, the EU’s approach to the maritime Global Space is to promote international governance regimes.

A software pipeline for automated wildlife population sampling

Ecologists today face significant challenges in accurately modeling wildlife populations. Population surveys provide an essential understanding of an ecosystem; however, they currently require an extensive amount of labor and resources to carry out which limits the frequency at which they are conducted. Lack of population data presents a significant barrier to ecologists in their ability to understand and model interactions between species and their surroundings. Preliminary work has been done in employing consumer drones and object detection software to automate data collection and processing on large mammal species. Such work suggests these technologies can significantly ease the process of data collection while maintaining an accuracy comparable to manual surveying techniques. While previous studies indicate the use of drone and object detection technology can aid in the collection of population data, there remain significant barriers in applying such methods to aid in ecological research on a broader scale. In particular, using object detection to identify target individuals involves combining many software tools, each of which comes with its own challenges and complexities. This paper presents a flexible software framework for automated population sampling that is accessible to researchers in the field of wildlife research. To achieve this we combine orthomosaic stitching, object detection, label post-processing, and visualization solutions into a single software pipeline. We then show how such a pipeline can be run in the cloud and provide documentation for others to replicate this process. Finally, we use a consumer drone and free navigation software to demonstrate the proposed workflow on a herd of cattle and assess its viability in providing useful population data.

Environmental cognitive load and spatial anxiety: What matters in navigation?

Spatial navigation is essential for orienting oneself in familiar and novel environments. Several internal factors may affect spatial navigation, such as anxiety. This study explored the mediating role of anxiety (spatial, trait and state) in the relationship between cognitive load and spatial navigation (egocentric and allocentric) using parallel mediation models. A sample of 125 participants (60 females) completed self-reported measures of anxiety (spatial and state-trait) during free navigation in a virtual reality square with or without environmental cognitive load. After the virtual learning experience, three spatial tasks evaluating landmark recognition and the use of egocentric and allocentric coordinates were administered. Results showed that spatial anxiety partially mediated the relationship between environmental load and spatial skills, specifically for egocentric and allocentric tasks. In addition, trait anxiety mediated the relationship between environmental load and allocentric performance. Overall, these findings update the Environmental Knowledge Model (EKM) that explains human navigation in terms of individual and environmental factors. The study results suggest that navigation could be facilitated by reducing anxiety and the spatial complexity of the environment.

Development of technology for standardization of parameters of winter route registration of game animals

The need to develop a new technology for planning survey routes is caused not only by the difficulty of implementing the recommendations of the current methodology for winter route accounting (ZMU) for the “equidistant placement” of survey routes with “observance of a proportional survey of land categories forest, field, swamp”, but also by the inability of the method of grouping the sample into categories of land to reduce the heterogeneity of the examined material. To standardize the placement of accounting routes, the technology of stratification of the territory is put, in which the virtual boundaries of the stratum serve to determine the extrapolation area and the location of the survey route. For the total area of the hunting farm, a tabular, or calculated (for farms with a different area), value of the route length standard, km/thousand ha, is established. The free navigation program SAS Planet determines the length and width of the territory within the boundaries of the hunting area and the principle layout of the registration routes in the strata. According to the algorithms, the following parameters are calculated: the total length of the routes, the average values of the length and width of the territory, the number of strata, the distance between the registration routes, the length of the route in the stratum, the area of the strata, which are necessary for designing the stratification of the territory. On the example of a specific farm with unknown values of parameters, except for the area of the territory, the suitability of algorithms for calculating parameters is shown; a map of the boundaries of the strata and the location of the accounting traces was created; the area of the strata and the design length of the registration route in each stratum were determined; digitized files of traces necessary for work on their reference to the terrain have been created; the technology of binding is given. The results of the study confirmed the manufacturability and simplicity of the method for localizing routes and extrapolation sites, as well as the compatibility of their ordered placement in compliance with a standardized sample size for territories with any area and boundary configuration.

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.

Application of the semantic network to manage processes in the area of legal regulation of education at the Doctoral School

Changes in legal regulations cause that moving in them can cause difficulties. Regulations scattered throughout various legal and normative acts or in the act itself may be problematic. This also affects people who are educated at Doctoral Schools. Situations in which the doctoral student is not sure of his status or the possibilities he can obtain financial resources may be problematic. The presented fragment of the semantic network is to be a tool that allows free navigation through the legal regulation regarding education at the doctoral school. Ultimately, the presented semantic network is a template that specific regulations of a given doctoral school can supplement.

Естественный и искусственный интеллект: точки соприкосновения

The study concerns the problem of the natural intelligence specificity which is cor­related with the prenatal stage of mind forming. At the same time, only those as­pects of the system’s functioning are the subject of consideration in research that become visible from the methodological perspective. These are the modes of non-duality and integrity of the very space of existence, in which not only the main po­tentialities of natural intelligence are laid, but the peculiarities of the contents that have their source in a given period are established also. When person becomes conscious such contents (and mechanisms for their provision) move into the un­conscious sphere. However, they continue to influence the entire universe of hu­man cognition in a significant way: they determine not only the nature and styles of thinking, but also set the cognition’s dynamics that are permissible in such a space of opportunity. In a generalized form, these contents can be characterized as intensional ones, while contents with which the dissociated mind of technocratic culture adult person deals are mainly extensional. Artificial intelligence, that has no knowledge of a non-dual mode of becoming, deals with the extensional facet of the natural mind modeling. To make an approximation to the potentials of natu­ral intelligence functioning more achievable, it is proposed to modify the structure of artificial intelligence systems’ “experience” by adding a mode of free navigation in the space provided for data analysis. Also it seems useful to modify the concept of rationality, by expanding it with two types of contents, whose genesis was corre­lated with non-dual-holistic mode of the mind functioning.

Evaluation of LiDAR for the Free Navigation in Agriculture

Driverless transport systems (DTS) or automated guided vehicles (AGV) have been part of intralogistics for over six decades. The uniform and structured environment conditions in industrial halls provided the ideal conditions for simple automation, such as in goods transport. Initially, implementing simply-designed safety devices, e.g., bumpers, could reduce risk to an acceptable level. However, these conditions are not present in an agricultural environment. Soiling and harsh weather conditions are anticipated both indoors and outdoors. The state of the art in intralogistics are light detection and ranging (LiDAR) scanners, which are suitable for both navigation and collision avoidance, including personal protection. In this study, the outdoor and navigation suitability of LiDAR is assessed in test series. The aim is to contribute advice on validation of LiDAR as a possible technology with respect to navigation and collision avoidance in freely navigating automatic feeding systems.

Natural Navigation System Design for Indoor Mobile Robots

Natural navigation simply refers to free navigation without the necessity of tapes, magnets, reflectors, or even wires. Many autonomous vehicles possess this as world maps are readily available and provide a perfect basis for machine learning solutions. However, this is not so much the case for indoor applications. Here, paths are often dynamic and more constrained; therefore, requiring the continuous identification, mapping and localization of the surrounding area. This work focuses on developing an indoor natural navigation system; the localization is achieved with a fusion of the wheel’s odometry to the on-board Inertial Measurement Unit (IMU i.e., a combination of relative localization and absolute localization) using Unscented Kalman Filter (UKF) as system’s encoder’s accumulation of errors is desired to be nullified while employing a PID control in correcting reference state errors. The map is simultaneously constructed using laws of geometry based on static points obtained from a Lidar, subsequently converted to an occupancy grid layout for effective path planning. In operation, tangency is applied in the avoidance of dynamic obstacles. The simulation results obtained in this study confirms the possibility of a simple, educational, indoor navigation system approach easily integrable by other mobile robots of the differential drive model.

An Environmental-Adaptability-Improved RatSLAM Method Based on a Biological Vision Model

Inspired by rodents’ free navigation through a specific space, RatSLAM mimics the function of the rat hippocampus to establish an environmental model within which the agent localizes itself. However, RatSLAM suffers from the deficiencies of erroneous loop-closure detection, low reliability on the experience map, and weak adaptability to environmental changes, such as lighting variation. To enhance environmental adaptability, this paper proposes an improved algorithm based on the HSI (hue, saturation, intensity) color space, which is superior in handling the characteristics of image brightness and saturation from the perspective of a biological visual model. The proposed algorithm first converts the raw image data from the RGB (red, green, blue) space into the HSI color space using a geometry derivation method. Then, a homomorphic filter is adopted to act on the I (intensity) channel and weaken the influence of the light intensity. Finally, guided filtering is used to process the S (saturation) channel and improve the significance of image details. The experimental results reveal that the improved RatSLAM model is superior to the original method in terms of the accuracy of visual template matching and robustness.

An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

A majority of collision avoidance and motion planning algorithms deployed on autonomous mobile robots tend to be reactive to the presence and motion of nearby dynamic actors. While these algorithms can produce collision free navigation, they do not necessarily follow social protocol and exhibit unnatural motions that force actors to behave very differently from their planned paths. As humans, we can reason about why and how we might <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interfere</i> with others and use this reasoning to alter our motion proactively. We also adapt our motion based on different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priorities</i> , which affect how we accommodate and interact with each other. In this letter, we propose an approach for a mobile robot to generate similar non-interfering and priority-based behaviors that pertain to how the robot accommodates dynamic actors. We augment a very efficient but reactive virtual physics-based planner with Hidden Markov Models and a Decision Tree-based monitor that: i) predicts if the robot will interfere with a nearby actor, ii) explains what factors cause the prediction, iii) plans priority-based corrective actions, and iv) updates the prediction model at runtime to improve and refine robot behaviors. Our approach is validated with simulations and extensive experiments on ground and aerial vehicles in the presence of dynamic actors.

ACO-Kinematic: a hybrid first off the starting block

The use of robots in carrying out various tasks is popular in many industries. In order to carry out a task, a robot has to move from one location to another using shorter, safer and smoother route. For movement, a robot has to know its destination, its previous location, a plan on the path it should take, a method for moving to the new location and a good understanding of its environment. Ultimately, the movement of the robot depends on motion planning and control algorithm. This paper considers a novel solution to the robot navigation problem by proposing a new hybrid algorithm. The hybrid algorithm is designed by combining the ant colony optimization algorithm and kinematic equations of the robot. The planning phase in the algorithm will find a route to the next step which is collision free and the control phase will move the robot to this new step. Ant colony optimization is used to plan a step for a robot and kinematic equations to control and move the robot to a location. By planning and controlling different steps, the hybrid algorithm will enable a robot to reach its destination. The proposed algorithm will be applied to multiple point-mass robot navigation in a multiple obstacle and line segment cluttered environment. In this paper, we are considering a priori known environments with static obstacles. The proposed motion planning and control algorithm is applied to the tractor-trailer robotic system. The results show a collision and obstacle free navigation to the target. This paper also measures the performance of the proposed algorithm using path length and convergence time, comparing it to a classical motion planning and control algorithm, Lyapunov based control scheme (LbCS). The results show that the proposed algorithm performs significantly better than LbCS including the avoidance of local minima.