Context Of Navigation Research Articles

Semantic Environment Atlas for Object-Goal Navigation

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.

Navigators’ Perspective on Information Requirements for Supervisory Control of Autonomous Ships

This study explores the application of agent transparency in the context of autonomous ships. Four levels of transparency were developed depicting decisions, planned actions, reasoning, and input parameters of a collision and grounding avoidance system in a realistic navigational context. Thirty-four licensed navigators were provided with Human Machine Interface concepts depicting four levels of transparency. Qualitative feedback was obtained through semi-structured interviews about which information they felt is needed to supervise autonomous ships safely and effectively. In addition, the participants’ ranked the HMIs according to their preferences. The results indicate the need for depicting the outcomes of the system’s collision risk analysis for supervisory control. Furthermore, the results illustrate the variations in supervisory strategies and the resulting dilemma for the amount and type of information required to support supervisors. Finally, this study highlights the importance of expert knowledge in the design of safety critical systems.

Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface ships

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

Enhancing Vehicle Navigation and Safety through Integration of Pre-Recorded Maps with Vehicle Control Unit

Powerful navigation and safety systems are vital to ensuring autonomous (DV) or semi-autonomous vehicles continue well in varied environmental conditions. However, in such a world those who depend on that internet connectivity to navigate their car would have problems in areas where network reliability is less than perfect. The purpose of this work is to investigate the feasibility and advantages of combining offline mapping with locally sensed positioning systems for better vehicle navigation and safety. While connected to the internet, vehicles cache offline maps and use local sensor information (such as GPS) from their inertial navigation system or computer vision without needing continuous access. The research outlines a hardware-software architecture with embedded offline map data storage, edge-based road following algorithms and an integration mechanism to advanced driving assistance systems (ADAS). In the evaluation of performance, accuracy assessments with online mapping systems are considered for offline maps and how these can have implications on end driver usability as well impacts on real-world autonomous driving technologies. The results suggest that offline mapping and on-board sensor-based localization would be able to improve vehicle contextual navigation performance in diverse driving scenarios.

SpaceLight: A Framework for Enhanced On-Orbit Navigation Imagery

In the domain of space rendezvous and docking, visual navigation plays a crucial role. However, practical applications frequently encounter issues with poor image quality. Factors such as lighting uncertainties, spacecraft motion, uneven illumination, and excessively dark environments collectively pose significant challenges, rendering recognition and measurement tasks during visual navigation nearly infeasible. The existing image enhancement methods, while visually appealing, compromise the authenticity of the original images. In the specific context of visual navigation, space image enhancement’s primary aim is the faithful restoration of the spacecraft’s mechanical structure with high-quality outcomes. To address these issues, our study introduces, for the first time, a dedicated unsupervised framework named SpaceLight for enhancing on-orbit navigation images. The framework integrates a spacecraft semantic parsing network, utilizing it to generate attention maps that pinpoint structural elements of spacecraft in poorly illuminated regions for subsequent enhancement. To more effectively recover fine structural details within these dark areas, we propose the definition of a global structure loss and the incorporation of a pre-enhancement module. The proposed SpaceLight framework adeptly restores structural details in extremely dark areas while distinguishing spacecraft structures from the deep-space background, demonstrating practical viability when applied to visual navigation. This paper is grounded in space on-orbit servicing engineering projects, aiming to address visual navigation practical issues. It pioneers the utilization of authentic on-orbit navigation images in the research, resulting in highly promising and unprecedented outcomes. Comprehensive experiments demonstrate SpaceLight’s superiority over state-of-the-art low-light enhancement algorithms, facilitating enhanced on-orbit navigation image quality. This advancement offers robust support for subsequent visual navigation.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies.

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK

Automatic navigation based on dual-antenna real-time kinematic (RTK) positioning has been widely employed for unmanned agricultural machinery, whereas GNSS inevitably suffers from signal blocking and electromagnetic interference. In order to improve the reliability of an RTK-based navigation system in a GNSS-challenged environment, an integrated navigation system is preferred for autonomous navigation, which increases the complexity and cost of the navigation system. The information fusion of integrated navigation has been dominated by Kalman filter (KF) for several decades, but the KF cannot assimilate the known knowledge of the navigation context efficiently. In this paper, the geometric characteristics of the straight path and path-tracking error were employed to formulate the constraint measurement model, which suppresses the position error in the case of RTK-degraded scenarios. The pseudo-measurements were then imported into the KF framework, and the smoothed navigation state was generated as a byproduct, which improves the reliability of the RTK positioning without external sensors. The experiment result of the mobile vehicle automatic navigation indicates that the tracking error-constrained KF (EC-KF) outperforms the trajectory-constrained KF (TC-KF) and KF when the RTK system outputs a float or single-point position (SPP) solution. In the case where the duration of the SPP solution was 20 s, the positioning errors of the EC-KF and TC-KF were reduced by 38.50% and 24.04%, respectively, compared with those of the KF.

Efficacy evaluation of adaptive collision avoidance systems for autonomous maritime surface ships based on target ships’ maneuvering behaviors

ABSTRACT Ongoing advancements in collision avoidance systems (CAS) for autonomous ships, built on diverse algorithms, have significantly enhanced performance. These systems, underpinned by a range of sophisticated algorithms, demonstrate increased accuracy in detecting potential collisions, quicker response times, and more reliable path prediction and avoidance maneuvers. Nevertheless, a prevalent trend in these systems is the insufficient consideration of the broader behavioral context of maritime navigation, especially for manned ships. The objective of this research is to present a methodology that can improve the capabilities of CAS in autonomous ships based on target ships’ maneuvering behaviors. This methodology will enable autonomous ships to respond adaptively to the actions of target ships, particularly manned ships, while also reacting to immediate environmental stimuli. The ultimate goal is to enhance the safety of navigation. This research utilizes information derived from simulated ship handling training, focusing on feature extraction within steering, spatial, and temporal elements. The tests prioritize the “Average Distance Deviation” metric for designing test scenarios. Simulation-based evaluation tests feature a velocity obstacle algorithm that discerns CAS action in single-ship crossing situations. The research reveals the potential of integrating behavioral dynamics into CAS, which enhances safety in autonomous maritime navigation and fosters a more integrated approach, where autonomous and manned ships coexist synergistically in shared environments.

Assessing the pre-implementation context for financial navigation in rural and non-rural oncology clinics.

Financial navigation (FN) is an evidence-based intervention designed to address financial toxicity for cancer patients. FN's success depends on organizations' readiness to implement and other factors that may hinder or support implementation. Tailored implementation strategies can support practice change but must be matched to the implementation context. We assessed perceptions of readiness and perceived barriers and facilitators to successful implementation among staff at nine cancer care organizations (5 rural, 4 non-rural) recruited to participate in the scale-up of a FN intervention. To understand differences in the pre-implementation context and inform modifications to implementation strategies, we compared findings between rural and non-rural organizations. We conducted surveys (n = 78) and in-depth interviews (n = 73) with staff at each organization. We assessed perceptions of readiness using the Organizational Readiness for Implementing Change (ORIC) scale. In-depth interviews elicited perceived barriers and facilitators to implementing FN in each context. We used descriptive statistics to analyze ORIC results and deductive thematic analysis, employing a codebook guided by the Consolidated Framework for Implementation Research (CFIR), to synthesize themes in barriers and facilitators across sites, and by rurality. Results from the ORIC scale indicated strong perceptions of organizational readiness across all sites. Staff from rural areas reported greater confidence in their ability to manage the politics of change (87% rural, 76% non-rural) and in their organization's ability to support staff adjusting to the change (96% rural, 75% non-rural). Staff at both rural and non-rural sites highlighted factors reflective of the Intervention Characteristics (relative advantage) and Implementation Climate (compatibility and tension for change) domains as facilitators. Although few barriers to implementation were reported, differences arose between rural and non-rural sites in these perceived barriers, with non-rural staff more often raising concerns about resistance to change and compatibility with existing work processes and rural staff more often raising concerns about competing time demands and limited resources. Staff across both rural and non-rural settings identified few, but different, barriers to implementing a novel FN intervention that they perceived as important and responsive to patients' needs. These findings can inform how strategies are tailored to support FN in diverse oncology practices.

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

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

Micro-Drone Ego-Velocity and Height Estimation in GPS-Denied Environments Using an FMCW MIMO Radar

In the context of autonomous navigation, the vehicle trajectory estimation and the detection of surrounding obstacles are two critical functionalities that must be robust to difficult environmental conditions (e.g., fog, dust, and snow) and the unavailability of infrastructure signals (e.g., GPS). With the advantage of remaining operable in low-visibility conditions, radar sensors are good candidates to detect obstacles in an autonomous navigation context. In this article, we show that radars can also be successfully used for real-time trajectory estimation. We address the case of an autonomous micro-drone intended for the exploration of piping networks and embedding a frequency-modulated continuous wave (FMCW) multi-input multioutput (MIMO) radar. We show that using a beamforming technique to virtually steer the radar field of view (FOV), we can simultaneously estimate the horizontal and vertical velocities of the drone as well as its height. These results are first validated through simulations based on experimental drone flight data and a radar simulator. Then, using an Infineon 77-GHz FMCW radar, we show, through real-world experiments, the high performance attainable with our solution.

Engagement with Master and Alternative Narratives of Gender and Sexuality Among LGBTQ+ Youth in the Digital Age

Gender and sexuality are contentious political issues in the US, with a resurgence of traditional master narratives for gender following decades of advances for gender equality. To understand how today’s LGBTQ+ youth navigate this narrative landscape in a polymedia context, we conducted social media tour interviews with 20 LGBTQ+ adolescents (aged 16–19), recording audiovisual data as they guided us through important posts on their top three public social media platforms. Through reflexive thematic analysis, we found that our participants were engaging with both longstanding master narratives (e.g., traditional gender roles) and contemporary alternative narratives (e.g., gender as non-binary) using three key navigational strategies for engaging with narratives on social media platforms: seeking and sharing information, creating queer community, and making choices about visibility and permanence. The meaning and purpose of these strategies for participants, both individually and collectively, could not be fully understood apart from three key navigational contexts: the traditional gender narrative, white liberal community context, and platform affordances. Our results demonstrate that narrative engagement for contemporary LGBTQ+ adolescents is deeply influenced by personal polymedia environments, identity intersections, and power structures shaping possibilities for individual identity expression and collective cultural transformation.

An intrinsic oscillator underlies visual navigation in ants

Many insects display lateral oscillations while moving, but how these oscillations are produced and participate in visual navigation remains unclear. Here, we show that visually navigating ants continuously display regular lateral oscillations coupled with variations of forward speed that strongly optimize the distance covered while simultaneously enabling them to scan left and right directions. This pattern of movement is produced endogenously and conserved across navigational contexts in two phylogenetically distant ant species. Moreover, the oscillations' amplitude can be modulated by both innate or learnt visual cues to adjust the exploration/exploitation balance to the current need. This lower-level motor pattern thus drastically reduces the degree of freedom needed for higher-level strategies to control behavior. The observed dynamical signature readily emerges from a simple neural circuit model of the insect's conserved pre-motor area known as the lateral accessory lobe, offering a surprisingly simple but effective neural control and endorsing oscillation as a core, ancestral way of moving in insects.

Hippocampal representations of foraging trajectories depend upon spatial context

Animals learn trajectories to rewards in both spatial, navigational contexts and relational, non-navigational contexts. Synchronous reactivation of hippocampal activity is thought to be critical for recall and evaluation of trajectories for learning. Do hippocampal representations differentially contribute to experience-dependent learning of trajectories across spatial and relational contexts? In this study, we trained mice to navigate to a hidden target in a physical arena or manipulate a joystick to a virtual target to collect delayed rewards. In a navigational context, calcium imaging in freely moving mice revealed that synchronous CA1 reactivation was retrospective and important for evaluation of prior navigational trajectories. In a non-navigational context, reactivation was prospective and important for initiation of joystick trajectories, even in the same animals trained in both contexts. Adaptation of trajectories to a new target was well-explained by a common learning algorithm in which hippocampal activity makes dissociable contributions to reinforcement learning computations depending upon spatial context.

Induction of spatial anxiety in a virtual navigation environment

Spatial anxiety (i.e., feelings of apprehension and fear about navigating everyday environments) can adversely impact people’s ability to reach desired locations and explore unfamiliar places. Prior research has either assessed spatial anxiety as an individual-difference variable or measured it as an outcome, but there are currently no experimental inductions to investigate its causal effects. To address this lacuna, we developed a novel protocol for inducing spatial anxiety within a virtual environment. Participants first learnt a route using directional arrows. Next, we removed the directional arrows and randomly assigned participants to navigate either the same route (n = 22; control condition) or a variation of this route in which we surreptitiously introduced unfamiliar paths and landmarks (n = 22; spatial-anxiety condition). The manipulation successfully induced transient (i.e., state-level) spatial anxiety and task stress but did not significantly reduce task enjoyment. Our findings lay the foundation for an experimental paradigm that will facilitate future work on the causal effects of spatial anxiety in navigational contexts. The experimental task is freely available via the Open Science Framework (https://osf.io/uq4v7/).

Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex.

Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given behavioral significance and how cortical networks might encode this information. We first demonstrate that rats can associate different speed patterns on the same trajectory with distinct behavioral choices. In this novel experimental paradigm, rats follow a small baited robot in a large megaspace environment where the rat's speed is precisely controlled by the robot's speed. Based on this proof of concept and research showing that recurrent reservoir networks are ideal for representing spatio-temporal structures, we then test reservoir networks in simulated navigation contexts and demonstrate they can discriminate between traversals of the same path with identical durations but different speed profiles. We then test the networks in an embodied robotic setup, where we use place cell representations from physically navigating robots as input and again successfully discriminate between traversals. To demonstrate that this capability is inherent to recurrent networks, we compared the model against simple linear integrators. Interestingly, although the linear integrators could also perform the speed profile discrimination, a clear difference emerged when examining information coding in both models. Reservoir neurons displayed a form of statistical mixed selectivity as a complex interaction between spatial location and speed that was not as abundant in the linear integrators. This mixed selectivity is characteristic of cortex and reservoirs and allows us to generate specific predictions about the neural activity that will be recorded in rat cortex in future experiments.

The impact of informal patient navigation initiatives on patient empowerment and National Health Insurance responsiveness in Indonesia

BackgroundIndonesia introduced a universal National Health Insurance (NHI) programme Jaminan Kesehatan Nasional (JKN) in 2014. However, challenges in timely consultation and access to health services resulted in the introduction of...

SEAN 2.0: Formalizing and Generating Social Situations for Robot Navigation

We present SEAN 2.0, an open-source system designed to advance social navigation via the training and benchmarking of navigation policies in varied social contexts. A key limitation of current social navigation research is that policies are often trained and evaluated considering only a few social contexts, which are fragmented across prior work. Inspired by work in psychology, we describe navigation context based on social situations, which encompass the robot task and environmental factors, and propose logic-based classifiers for five common examples. SEAN 2.0 allows a robot to experience these social situations via different methods for specifying and generating pedestrian motion, including a novel Behavior Graph method. Our experiments show that when data collected using the Behavior Graph method is used to learn a robot navigation policy, that policy outperforms others trained using alternative methods for pedestrian control. Also, social situations were found to be useful for understanding performance across social contexts. Other components of SEAN 2.0 include vision and depth sensors, several physical environments, different means of specifying robot tasks, and a range of evaluation metrics for social robot navigation. User feedback for SEAN 2.0 indicated that the system was “easier to navigate and more user friendly” than SEAN 1.0.

Investigating Four Navigation Aids for Supporting Navigator Performance and Independence in Virtual Reality

Turn-by-turn navigation guidance is suggested to impair users’ independent wayfinding in the physical world. However, whether this impairment issue also exists in a virtual environment is underexplored. We compare map-based and live view-based turn-by-turn navigation aids with two additional navigation aids, reference-based and orientation-based, designed to provide directional knowledge. The results of our within-subjects experiment indicate that turn-by-turn navigation aids performed worse in building spatial awareness than reference-based guidance in virtual reality. In unaided wayfinding, reference-based guidance helped users navigate most efficiently. Unexpectedly, orientation-based guidance yielded poor navigation performance, similar to the turn-by-turn navigation aids. We found that the key to skill impairment is navigators’ tendency to rely on automated instructions. This suggests that turn-by-turn navigation aids need not be avoided, but rather that caution should be exercised to avoid the tendency of mindless instruction-following. Our qualitative findings also suggest the crucial roles of the navigation context and the suitability of the navigation aid for the context.

Air Traffic Controllers and Executive Brain Function.

BACKGROUND: This study evaluated the executive functions of air traffic controllers (ATCs) in relation to demographic and occupational characteristics such as length of service, technical qualifications, and work shifts.METHODS: This was a cross-sectional study based on a convenience sample with sequential selection of 52 Brazilian ATCs using the Wisconsin Card Sorting Test (WCST), questionnaire applied to the ATCs, Student's t-test, and one-way analysis of variance with post hoc Tukey multiple comparisons of WCST with functional characteristics.RESULTS: ATCs with 0 to 5 yr of service presented scores significantly above the cohort average in the WCST [0-5 yr: 0.54 ± 0.01 vs. 6-15 yr: 0.31 ± 0.52 vs. 151 yr: -0.02 ± 0.80]. ATCs working a 3-shift pattern presented an efficient performance and fewer perseverative errors in the WCST (3-shift: -0.63 ± 0.38 vs. 4-shift: -0.45 ± 0.43), that did not rise to significance. In a comparison between executive brain functions and technical qualifications, the controllers who worked in the TWR (Aerodrome Control Tower) only, and those who worked in both the TWR and APP (Approach Control Service) showed no differences in the number of completed categories and in perseverative errors.DISCUSSION:The executive brain functioning of the ATCs, such as mental flexibility, strategic planning and inhibitory control, were identified as being above average when compared to the general population. While alterations in work shifts appear to have a negative (but nonsignificant) impact, newer ATCs showed stronger scores than more experienced ATCs on the WCST. Successful performance as an ATC has complex foundations, such as understanding the context of air navigation and having strong executive function capabilities.de Freitas AM, Portuguez MW, Russomano, T, da Costa JC. Air traffic controllers and executive brain function. Aerosp Med Hum Perform. 2022; 93(5):426-432.