Navigation Ability Research Articles

Interaction of Cognitive and Affective Load Within a Virtual City

Spatial navigation is an important aspect of everyday life but may be negatively impacted by both cognitive and affective load. Cognitive and affective load may be measured via autonomic arousal and increased load may lead to reduced navigational abilities. 53 college students (64.0% female; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> age = 19.62) participated in the Virtual Reality Paced Serial Auditory Addition Task (VR-PASAT). Participants followed guides through different areas of the virtual environment (VE). In some areas participants completed the PASAT, high load, and in other areas they simply followed the guides, low load. Some participants were instructed beforehand they would perform a navigate task, increasing load. Results suggested that several psychophysiological measures including skin conductance and inter-beat intervals were impacted by increased load while others were related to the interactions between load and zone order. Awareness of the navigation task led to worse performance on the VR-PASAT, and high load decreased navigational performance. The VR-PASAT was used to implement a VE to manipulate cognitive load. This study may be useful for the creation of adaptive systems because it demonstrates that psychophysiological metrics can assess cognitive and affective load, which may impact navigation within a VE, and navigational task awareness may interact with load.

An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism.

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions.

Directed retreat and navigational mechanisms in trail following Formica obscuripes

Ant species exhibit behavioural commonalities when solving navigational challenges for successful orientation and to reach goal locations. These behaviours rely on a shared toolbox of navigational strategies that guide individuals under an array of motivational contexts. The mechanisms that support these behaviours, however, are tuned to each species’ habitat and ecology with some exhibiting unique navigational behaviours. This leads to clear differences in how ant navigators rely on this shared toolbox to reach goals. Species with hybrid foraging structures, which navigate partially upon a pheromone-marked column, express distinct differences in their toolbox, compared to solitary foragers. Here, we explore the navigational abilities of the Western Thatching ant (Formicaobscuripes), a hybrid foraging species whose navigational mechanisms have not been studied. We characterise their reliance on both the visual panorama and a path integrator for orientation, with the pheromone’s presence acting as a non-directional reassurance cue, promoting continued orientation based on other strategies. This species also displays backtracking behaviour, which occurs with a combination of unfamiliar terrestrial cues and the absence of the pheromone, thus operating based upon a combination of the individual mechanisms observed in solitarily and socially foraging species. We also characterise a new form of goalless orientation in these ants, an initial retreating behaviour that is modulated by the forager’s path integration system. The behaviour directs disturbed inbound foragers back along their outbound path for a short distance before recovering and reorienting back to the nest.

Real-World Navigation With Application: Characteristics of Gaze Behavior and Associated Factors in Older Adults.

Advancing age might impair real-world navigation ability. The use of mobile devices by older adults has grown rapidly in recent years. Navigation applications (apps) in mobile devices may facilitate the freedom of outings for older adults. Our aim is to investigate age-related differences in real-world app-based navigation walking in terms of accuracy, efficiency, and gaze behavior; and to explore clinical factors associated with navigation walking in older adults. A total of 20 community-dwelling older adults and 16 young adults completed a route navigation task using a navigation app while recording their gaze behavior. Outcomes were compared in both groups and a general linear regression was used to explore clinical factors associated with app-based navigation walk in older adults. Compared with young participants, older participants had more stops and root errors and less fixation time, smaller amplitude of saccades. Additionally, older adults were more likely to glance at their smartphones while app-based navigation walking. Furthermore, gait speed and the following assessment scores were significantly associated with navigation walking in older adults: Mini-Mental State Examination, Life-Space Assessment, and the short version of the Mobile Device Proficiency Questionnaire. For app-based navigation walks, differences in accuracy and gaze behavior were found to exist with age. Additionally, efficient real-world navigation walks in older adults require the extent of life space and proficiency with mobile devices, along with walking speed and cognitive function. It is possible that age-related functional decline, such as the visual field and shifting attention between mobile devices and the real world, may have influenced the results. The study also suggests the need to understand the level of proficiency with mobile devices so that older adults can continue to go out freely. These findings give the basis for providing older adults with appropriate navigation assistance.

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism.

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.

Camera-based automated monitoring of flying insects in the wild (Camfi). II. flight behaviour and long-term population monitoring of migratory Bogong moths in Alpine Australia.

The Bogong moth Agrotis infusa is well known for its remarkable annual round-trip migration from its breeding grounds across eastern and southern Australia to its aestivation sites in the Australian Alps, to which it provides an important annual influx of nutrients. Over recent years, we have benefited from a growing understanding of the navigational abilities of the Bogong moth. Meanwhile, the population of Bogong moths has been shrinking. Recently, the ecologically and culturally important Bogong moth was listed as endangered by the IUCN Red List, and the establishment of a program for long-term monitoring of its population has been identified as critical for its conservation. Here, we present the results of two years of monitoring of the Bogong moth population in the Australian Alps using recently developed methods for automated wildlife-camera monitoring of flying insects, named Camfi. While in the Alps, some moths emerge from the caves in the evening to undertake seemingly random flights, filling the air with densities in the dozens per cubic metre. The purpose of these flights is unknown, but they may serve an important role in Bogong moth navigation. We found that these evening flights occur throughout summer and are modulated by daily weather factors. We present a simple heuristic model of the arrival to and departure from aestivation sites by Bogong moths, and confirm results obtained from fox-scat surveys which found that aestivating Bogong moths occupy higher elevations as the summer progresses. Moreover, by placing cameras along two elevational transects below the summit of Mt. Kosciuszko, we found that evening flights were not random, but were systematically oriented in directions relative to the azimuth of the summit of the mountain. Finally, we present the first recorded observations of the impact of bushfire smoke on aestivating Bogong moths - a dramatic reduction in the size of a cluster of aestivating Bogong moths during the fire, and evidence of a large departure from the fire-affected area the day after the fire. Our results highlight the challenges of monitoring Bogong moths in the wild and support the continued use of automated camera-based methods for that purpose.

Generalization Ability of Deep Reinforcement Learning-based Navigation Based on Curriculum Learning

In this paper, curriculum learning is studied as an approach to improve generalization ability in navigation tasks, that is, improve the agent’s ability of navigating in scenarios different from those used for training. The agent is trained based on the TD3 algorithm, and curriculum learning selects different stages (i.e. different curricula) of Empty/Sparse/Normal worlds for training. Via extensive numerical comparisons with agents trained under such curricula, it is shown that properly used curriculum learning improves the agent’s ability of generalization. Furthermore, an automatic curriculum learning (Auto-CL) approach is proposed. Auto-CL is shown to have even better generalization than the standard curriculum learning, since it makes the agent able to navigate in new environments with more than 6% shorter paths in more than 21% shorter time.

Getting to the Objective: Analyzing Navigation, Automation, and Situation Awareness from a Soldier’s Perspective

Land navigation ability is essential for US Army soldiers, who must use it both when planning routes to an objective and while executing the mission itself. Soldiers use automation to offload the cognitive effort of navigation, but if the system fails soldiers must expend precious time and mental energy reacquiring their location on a map. Despite this risk, soldiers still over-trust automated navigation and lose their location-related situation awareness. Loss of situation awareness leads to an inability to acquire the necessary spatial knowledge required to manually navigate. This paper reviews existing research to examine the effects of automation on situation awareness and a user’s ability to build a cognitive map from a soldier’s perspective. It further identifies issues with current technology and suggests possible directions for future research.

Clinical utility of self- and informant-reported memory, attention, and spatial navigation in detecting biomarkers associated with Alzheimer disease in clinically normal adults.

Preclinical Alzheimer disease (AD) has been associated with subtle changes in memory, attention, and spatial navigation abilities. The current study examined whether self- and informant-reported domain-specific cognitive changes are sensitive to AD-associated biomarkers. Clinically normal adults aged 56-93 and their informants completed the memory, divided attention, and visuospatial abilities (which assesses spatial navigation) subsections of the Everyday Cognition Scale (ECog). Reliability and validity of these subsections were examined using Cronbach's alpha and confirmatory factor analysis. Logistic regression was used to examine the ability of ECog subsections to predict AD-related biomarkers (cerebrospinal fluid (CSF) ptau181/Aβ42 ratio (N = 371) or hippocampal volume (N = 313)). Hierarchical logistic regression was used to examine whether the self-reported subsections continued to predict biomarkers when controlling for depressive symptomatology if available (N = 197). Additionally, logistic regression was used to examine the ability of neuropsychological composites assessing the same or similar cognitive domains as the subsections (memory, executive function, and visuospatial abilities) to predict biomarkers to allow for comparison of the predictive ability of subjective and objective measures. All subsections demonstrated appropriate reliability and validity. Self-reported memory (with outliers removed) was the only significant predictor of AD biomarker positivity (i.e., CSF ptau181/Aβ42 ratio; p = .018) but was not significant when examined in the subsample with depressive symptomatology available (p = .517). Self-reported memory (with outliers removed) was a significant predictor of CSF ptau181/Aβ42 ratio biomarker positivity when the objective memory composite was included in the model. ECog subsections were not robust predictors of AD biomarker positivity.

Dissociable Cognitive Systems for Recognizing Places and Navigating through Them: Developmental and Neuropsychological Evidence.

Recent neural evidence suggests that the human brain contains dissociable systems for "scene categorization" (i.e., recognizing a place as a particular kind of place, for example, a kitchen), including the parahippocampal place area, and "visually guided navigation" (e.g., finding our way through a kitchen, not running into the kitchen walls or banging into the kitchen table), including the occipital place area. However, converging behavioral data - for instance, whether scene categorization and visually guided navigation abilities develop along different timelines and whether there is differential breakdown under neurologic deficit - would provide even stronger support for this two-scene-systems hypothesis. Thus, here we tested scene categorization and visually guided navigation abilities in 131 typically developing children between 4 and 9 years of age, as well as 46 adults with Williams syndrome, a developmental disorder with known impairment on "action" tasks, yet relative sparing on "perception" tasks, in object processing. We found that (1) visually guided navigation is later to develop than scene categorization, and (2) Williams syndrome adults are impaired in visually guided navigation, but not scene categorization, relative to mental age-matched children. Together, these findings provide the first developmental and neuropsychological evidence for dissociable cognitive systems for recognizing places and navigating through them.SIGNIFICANCE STATEMENT Two decades ago, Milner and Goodale showed us that identifying objects and manipulating them involve distinct cognitive and neural systems. Recent neural evidence suggests that the same may be true of our interactions with our environment: identifying places and navigating through them are dissociable systems. Here we provide converging behavioral evidence supporting this two-scene-systems hypothesis - finding both differential development and breakdown of "scene categorization" and "visually guided navigation." This finding suggests that the division of labor between perception and action systems is a general organizing principle for the visual system, not just a principle of the object processing system in particular.

Dissociating effects of aging and genetic risk of sporadic Alzheimer’s disease on path integration

Path integration is a spatial navigation ability that requires the integration of information derived from self-motion cues and stable landmarks, when available, to return to a previous location. Path integration declines with age and Alzheimer’s disease (AD). Here, we sought to separate the effects of age and AD risk on path integration, with and without a landmark. Overall, 279 people participated, aged between 18 and 80 years old. Advanced age impaired the appropriate use of a landmark. Older participants furthermore remembered the location of the goal relative to their starting location and reproduced this initial view without considering that they had moved in the environment. This lack of adaptative behavior was not associated with AD risk. In contrast, participants at genetic risk of AD (apolipoprotein E ε4 carriers) exhibited a pure path integration deficit, corresponding to difficulty in performing path integration in the absence of a landmark. Our results show that advanced-age impacts landmark-supported path integration, and that this age effect is dissociable from the effects of AD risk impacting pure path integration.

Using a picture (or a thousand words) for supporting spatial knowledge of a complex virtual environment

External representations powerfully support and augment complex human behavior. When navigating, people often consult external representations to help them find the way to go, but do maps or verbal instructions improve spatial knowledge or support effective wayfinding? Here, we examine spatial knowledge with and without external representations in two studies where participants learn a complex virtual environment. In the first study, we asked participants to generate their own maps or verbal instructions, partway through learning. We found no evidence of improved spatial knowledge in a pointing task requiring participants to infer the direction between two targets, either on the same route or on different routes, and no differences between groups in accurately recreating a map of the target landmarks. However, as a methodological note, pointing was correlated with the accuracy of the maps that participants drew. In the second study, participants had access to an accurate map or set of verbal instructions that they could study while learning the layout of target landmarks. Again, we found no evidence of differentially improved spatial knowledge in the pointing task, although we did find that the map group could recreate a map of the target landmarks more accurately. However, overall improvement was high. There was evidence that the nature of improvement across all conditions was specific to initial navigation ability levels. Our findings add to a mixed literature on the role of external representations for navigation and suggest that more substantial intervention—more scaffolding, explicit training, enhanced visualization, perhaps with personalized sequencing—may be necessary to improve navigation ability.

Space use and navigation ability of hens at housing in the aviary for the laying phase: effect of enrichment with additional perches and genotype

The present study tested the hypothesis that increased availability of perches could favor the adaptation and navigation ability of pullets of different genotypes at housing in a new aviary system for the laying phase. To this purpose, 900 Lohmann White-LSL and 900 Hy-line Brown were randomly allocated at 17 wk of age in 8 pens of an experimental aviary, according to a bifactorial arrangement with 2 genotypes (Brown vs. White)×2 types of pens (enriched or not enriched with additional perches besides those of the aviary). Data collected between 17 and 20 wk of age showed that the enrichment with additional perches decreased the use of the aviary perches while the rate of successful landings/take-offs was unaffected. As for the effect of genotype, during the night a lower rate of hens on the floor (0.15 vs. 6.63%) and a higher rate of hens on the additional perches (2.47 vs. 0.98%) was found in White compared to Brown hens (P < 0.001); the former hens also used the third tiers for sleeping on the aviary uppermost perches (P < 0.001). During the day, White hens used more the third tier (32.8 vs. 15.6%; P < 0.001) and the additional perches (3.88 vs. 0.91%; P < 0.01) compared to Brown hens, while they stood less on the floor (18.3 vs. 22.6%; P < 0.05). White hens performed a significantly higher number of landings (80.7 vs. 21.9; P < 0.001) and of take-offs (74.3 vs. 10.0; P < 0.001) per pen compared to Brown hens. The risk of unsuccessful landings was higher in Brown compared to White hens (odd ratio: 6.65; 95% confidence interval: 4.36-10.1; P < 0.001). In conclusion, the enrichment with additional perches played a major role in hen distribution and space use than in their navigation ability. At the same time, the significant differences between the 2 genotypes call for a careful evaluation of the aviary design and animal management to optimize welfare at housing and possibly productive results of laying hens.

A Machine-Learning-Based Method for Ship Propulsion Power Prediction in Ice

In recent years, safety issues respecting polar ship navigation in the presence of ice have become a research hotspot. The accurate prediction of propulsion power plays an important role in ensuring safe ship navigation and evaluating ship navigation ability, and deep learning has been widely applied in the field of shipping, of which the artificial neural network (ANN) is a common method. This study combines the scientific problems of ice resistance and propulsion power for polar ship design, focusing on the design of an ANN model for predicting the propulsion power of polar ships. Reference is made to the traditional propulsion power requirements of various classification societies, as well as ship model test and full-scale test data, to select appropriate input features and a training dataset. Three prediction methods are considered: building a radial basis function–particle swarm optimization algorithm (RBF-PSO) model to directly predict the propulsion power; based on the full-scale test and model test data, calculating the propulsion power using the Finnish–Swedish Ice Class Rules (FSICR) formula; using an ice resistance artificial neural network model (ANN-IR) to predict the ice resistance and calculate the propulsion power using the FSICR formula. Prediction errors are determined, and a sensitivity analysis is carried out with respect to the relevant parameters of propulsion power based on the above methods. This study shows that the RBF-PSO model based on nine feature inputs has a reasonable generalization effect. Compared with the data of the ship model test and full-scale test, the average error is about 14%, which shows that the method has high accuracy and can be used as a propulsion power prediction tool.

Cultural determinants of the gap between self-estimated navigation ability and wayfinding performance: evidence from 46 countries

Cognitive abilities can vary widely. Some people excel in certain skills, others struggle. However, not all those who describe themselves as gifted are. One possible influence on self-estimates is the surrounding culture. Some cultures may amplify self-assurance and others cultivate humility. Past research has shown that people in different countries can be grouped into a set of consistent cultural clusters with similar values and tendencies, such as attitudes to masculinity or individualism. Here we explored whether such cultural dimensions might relate to the extent to which populations in 46 countries overestimate or underestimate their cognitive abilities in the domain of spatial navigation. Using the Sea Hero Quest navigation test and a large sample (N = 383,187) we found cultural clusters of countries tend to be similar in how they self-rate ability relative to their actual performance. Across the world population sampled, higher self-ratings were associated with better performance. However, at the national level, higher self-ratings as a nation were not associated with better performance as a nation. Germanic and Near East countries were found to be most overconfident in their abilities and Nordic countries to be most under-confident in their abilities. Gender stereotypes may play a role in mediating this pattern, with larger national positive attitudes to male stereotyped roles (Hofstede's masculinity dimension) associated with a greater overconfidence in performance at the national level. We also replicate, with higher precision than prior studies, evidence that older men tend to overestimate their navigation skill more than other groups. These findings give insight into how culture and demographics may impact self-estimates of our abilities.

Satellite navigation method based on high-speed frequency hopping signal

Global navigation satellite system has been widely used, but it is vulnerable to jamming. In military satellite communications, frequency hopping (FH) signal is usually used for anti-jamming communications. If the FH signal can be used in satellite navigation, the anti-jamming ability of satellite navigation can be improved. Although a recently proposed time-frequency matrix ranging method (TFMR) can use FH signals to realize pseudorange measurement, it cannot transmit navigation messages using the ranging signal which is crucial for satellite navigation. In this article, we propose dual-tone binary frequency shift keying-based TFMR (DBFSK-TFMR). DBFSK-TFMR designs an extended time-frequency matrix (ETFM) and its generation algorithm, which can use the frequency differences in different dual-tone signals in ETFM to modulate data and eliminate the negative impact of data modulation on pseudorange measurement. Using ETFM, DBFSK-TFMR not only realizes the navigation message transmission but also ensures the precision and unambiguous measurement range of pseudorange measurement. DBFSK-TFMR can be used as an integrated solution for anti-jamming communication and navigation based on FH signals. Simulation results show that DBFSK-TFMR has almost the same ranging performance as TFMR.

RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: https://github.com/tencent-ailab/RLogist.

Study on the Classification Perception and Visibility Enhancement of Ship Navigation Environments in Foggy Conditions

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.

Cooperative behavior of miniature magnetic swimmers in uniform magnetic fields

Magnetically powered miniature robots have received increasing attention for decades owing to their convenient navigation ability, noninvasive remote actuation, and diverse potential applications. Although various individual motions of magnetic miniature robots have been explored, mimicking group cooperation and synchronized coordination of natural intelligence remain challenging. Here, we introduce diverse cooperative behaviors between two magnetic miniature swimmers in uniform magnetic fields by tuning and composing their magnetic moments. The ability of these magnetic miniature swimmers to form dynamic steady states offers considerable promise for creating groups of machines that can perform cooperative tasks.

Robot morphology evolution for automated HVAC system inspections using graph heuristic search and reinforcement learning

The building sector consumes more than 70% of the electricity produced in the U.S., with Heating Ventilation and Air Conditioning (HVAC) systems accounting for half of the electricity consumption. Leaks in HVAC systems have a significant impact on the energy efficiency of buildings, resulting in up to 33% of energy loss. However, the current manual approach to inspection is time-consuming and reactive, leaving room for automation. This paper presents a framework to automatically evolve robot morphology without requiring human intervention to suite any given HVAC and ceiling design. Robot morphologies are optimized using graph heuristic search based on tasks and environment designs, followed by testing of navigation abilities of the best-evolved robot in diverse ceiling environments using reinforcement learning. Tests conducted in robot simulation tools, utilizing realistic HVAC designs retrieved from Building Information Models (BIMs), demonstrate that effortless navigation in complex ceiling environments can be achieved by the evolved robots.