Navigation Information Research Articles

Parahippocampal neurons encode task-relevant information for goal-directed navigation

A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal’s position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal’s behavioral performance.

Parahippocampal neurons encode task-relevant information for goal-directed navigation.

A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal's position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance.

Circular-Feature-Based Pose Estimation of Noncooperative Satellite Using Time-of-Flight Sensor

This paper develops an end-to-end circular-feature-based pose estimation using the time-of-flight (TOF) camera for capturing serviced satellite. First, the docking ring is chosen as the recognized object for pose measurement. Second, a new ellipse detection method is proposed to detect the docking ring. Third, the circular feature is used to estimate the six-degrees-of-freedom pose with the aid of a reflective block. Combined with the range at each pixel location from TOF, the pose duality can be eliminated, and the roll angle is recovered through the geometric constraint. Ground physical experiments validate the effectiveness and efficiency of the proposed method. The on-orbit application demonstrates that the proposed algorithm can accurately and robustly estimate the relative pose of the noncooperative satellite, providing reliable navigation information for the chaser spacecraft with the relative pose estimation errors less than 2 deg and 5 cm.

A Robust Track Estimation Method for Airborne SAR Based on Weak Navigation Information and Additional Envelope Errors

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm.

The bearings and distances isolines combinations of the navigational landmarks pair for the vessel fixed position determination

The issues of applying basic navigation parameters (bearing and distance), and their isolines traditionally used in navigation practice in coastal waters are studied in the paper. A new distinctive feature is the use of isolines combinations (three distance arcs, two isoazimuths, a hyperbola and an ellipse) based on measured bearings and distances to the pair of landmarks to obtain the vessel fixed positions with high precision. The traditional navigation methods (the theory of isolines, the generalized method of lines position, the method of least squares) are involved as a mathematical framework. The mathematical argumentation and the isolines characteristics of bearings and distances, their combinations (difference and sum of distances, difference of bearings) are given. The solutions of the surplus equations of the lines position under random error influence by the least squares method and the accuracy assessment of the vessel fix position by the radial error including the reduced variant to compensate the systematic errors are suggested. The principal points of the research are supported by the graphic interpretation, and the given expressions are adjusted to the practical application and navigation systems software development. The computer simulation that shows the radial error of the fixed position by seven position lines is at least two times less than by any pair of position lines has been carried out. The formalization of the proposed methods in the automatic navigation systems or in autonomous ship control systems will enable the navigator on board or at operating the vessel remotely to solve the problems of the navigation information processing in coastal and congested waters on a new level.

Distinct manifold encoding of navigational information in the subiculum and hippocampus.

The subiculum (SUB) plays a crucial role in spatial navigation and encodes navigational information differently from the hippocampal CA1 area. However, the representation of subicular population activity remains unknown. Here, we investigated the neuronal population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The trajectory of population activity in both areas was confined to low-dimensional neural manifolds homoeomorphic to external space. The manifolds conveyed position, speed, and future path information with higher decoding accuracy in the SUB than in the CA1. The manifolds exhibited common geometry across rats and regions for the CA1 and SUB and between tasks in the SUB. During post-task ripples in slow-wave sleep, population activity represented reward locations/events more frequently in the SUB than in CA1. Thus, the CA1 and SUB encode information distinctly into the neural manifolds that underlie navigational information processing during wakefulness and sleep.

YOLOv7-RepFPN: Improving real-time performance of laparoscopic tool detection on embedded systems.

This study focuses on enhancing the inference speed of laparoscopic tool detection on embedded devices. Laparoscopy, a minimally invasive surgery technique, markedly reduces patient recovery times and postoperative complications. Real-time laparoscopic tool detection helps assisting laparoscopy by providing information for surgical navigation, and its implementation on embedded devices is gaining interest due to the portability, network independence and scalability of the devices. However, embedded devices often face computation resource limitations, potentially hindering inference speed. To mitigate this concern, the work introduces a two-fold modification to the YOLOv7 model: the feature channels and integrate RepBlock is halved, yielding the YOLOv7-RepFPN model. This configuration leads to a significant reduction in computational complexity. Additionally, the focal EIoU (efficient intersection of union) loss function is employed for bounding box regression. Experimental results on an embedded device demonstrate that for frame-by-frame laparoscopic tool detection, the proposed YOLOv7-RepFPN achieved an mAP of 88.2% (with IoU set to 0.5) on a custom dataset based on EndoVis17, and an inference speed of 62.9 FPS. Contrasting with the original YOLOv7, which garnered an 89.3% mAP and 41.8 FPS under identical conditions, the methodology enhances the speed by 21.1 FPS while maintaining detection accuracy. This emphasizes the effectiveness of thework.

Neuronal sensorimotor integration guiding salt concentration navigation in Caenorhabditis elegans

Animals navigate their environment by manipulating their movements and adjusting their trajectory which requires a sophisticated integration of sensory data with their current motor status. Here, we utilize the nematode Caenorhabditis elegans to explore the neural mechanisms of processing the sensory and motor information for navigation. We developed a microfluidic device which allows animals to freely move their heads while receiving temporal NaCl stimuli. We found that C. elegans regulates neck bending direction in response to temporal NaCl concentration changes in a way which is consistent with a C. elegans' navigational strategy which regulates traveling direction toward preferred NaCl concentrations. Our analysis also revealed that the activity of a neck motor neuron is significantly correlated with neck bending and activated by the decrease in NaCl concentration in a phase-dependent manner. By combining the analysis of behavioral and neural response to NaCl stimuli and optogenetic perturbation experiments, we revealed that NaCl decrease during ventral bending activates the neck motor neuron which counteracts ipsilateral bending. Simulations further suggest that this phase-dependent response of neck motor neurons can facilitate curving toward preferred salt concentrations.

Event-Assisted Object Tracking on High-Speed Drones in Harsh Illumination Environment

Drones have been used in a variety of scenarios, such as atmospheric monitoring, fire rescue, agricultural irrigation, etc., in which accurate environmental perception is of crucial importance for both decision making and control. Among drone sensors, the RGB camera is indispensable for capturing rich visual information for vehicle navigation but encounters a grand challenge in high-dynamic-range scenes, which frequently occur in real applications. Specifically, the recorded frames suffer from underexposure and overexposure simultaneously and degenerate the successive vision tasks. To solve the problem, we take object tracking as an example and leverage the superior response of event cameras over a large intensity range to propose an event-assisted object tracking algorithm that can achieve reliable tracking under large intensity variations. Specifically, we propose to pursue feature matching from dense event signals and, based on this, to (i) design a U-Net-based image enhancement algorithm to balance RGB intensity with the help of neighboring frames in the time domain and then (ii) construct a dual-input tracking model to track the moving objects from intensity-balanced RGB video and event sequences. The proposed approach is comprehensively validated in both simulation and real experiments.

Nighttime bionic compass based on a short-wave infrared polarization sensing system.

Sky-bionic polar co-ordinate navigation is an effective means of providing navigational information in the absence of a priori information. Polar co-ordinate navigation during clear daytime conditions has been studied, but there has been a lack of research of it at night due to problems with noise. Therefore, in this paper, a short-wave infrared polarimetric sensor system is designed, which is capable of acquiring atmospheric polarimetric information in low illumination environments at night, compared with traditional visible band sensors. Additionally, based on the statistics of polarization angle information, an algorithm for removing noise and starlight is proposed to solve the influence of starlight and noise on the polarization information at night. After many outdoor experiments, we found that the method can output the heading angle stably and accurately, and its standard deviation is controlled to be 0.42° in a clear night.

Enhancing spatial navigation skills in mild cognitive impairment patients: a usability study of a new version of ANTaging software.

Mild Cognitive Impairment (MCI) often presents challenges related to spatial navigation and retention of spatial information. Navigating space involves intricate integration of bodily and environmental cues. Spatial memory is dependent on two distinct frame of reference systems for organizing this information: egocentric and allocentric frames of reference. Virtual Reality (VR) has emerged as a promising technology for enhancing spatial navigation skills and spatial memory by facilitating the manipulation of bodily, environmental, and cognitive cues. This usability study was based on a fully within-subjects design in which seven MCI patients underwent two kinds of VR conditions: participants were required to complete the ANTaging demo both in Oculus Rift S (immersive condition) and in Samsung UHD 4K monitor (semi-immersive condition). Participants were seated and they had to use a foot-motion pad to navigate and explore the environment to collect and relocate some objects in the virtual environment. Post-interaction, users provided feedback on their experiences. Additionally, usability, potential side effects, data analysis feasibility, and user preferences with immersive and semi-immersive technologies were assessed through questionnaires. Results indicated higher usability ratings for the semi-immersive setup, with fewer negative effects reported compared to the immersive counterpart. According to qualitative analyses of the interviews, patients do seem to like both VR apparatuses even though the semi-immersive condition was perceived as the most suitable choice because of the size of the screen. Patients generally found it difficult to remember object locations. Participants expressed the need for more practice with the foot-motion pad, despite an overall positive experience. They generally would like to use this system to improve their memory. Identifying these key aspects was crucial for refining the system before the upcoming clinical trial. This study sheds light on the potential of semi-immersive VR in aiding individuals with MCI, paving the way for enhanced spatial navigation interventions.

Research of adaptive algorithms of recurrent filtration at processing data from GNSS when radio signals lose from navigation satellites

Algorithms for linear and adaptive Kalman filtering of navigation data received from satellite navigation systems are considered. A comparison of the Kalman linear filter algorithms, the Yazvinsky adaptive filter, the adaptive filter with feedback on the updated sequence and the adaptive filter without a priori information about the noise covariance matrices is made. The algorithms are tested in a practical experiment — navigation information from GNSS is obtained by using a GNSS receiver installed on a car moving in an urban environment. The comparative analysis results of the algorithms usage for adaptive modifications of the Kalman filter are presented. The results showed that the Yazvinsky filter works worse when radio signals from navigation satellites disappear, and the adaptive filter algorithm without a priori information about the noise covariance matrices does not actually filter the data. Keywords GNSS, navigation information, Kalman filter, Yazvinsky filter, adaptive Kalman filter

START Foundation: Coping with Bias and Fairness when Implementing and Using an AI System

START Foundation annually supports nearly 200 young people with a migratory background through an individualized scholarship program aimed at personal development and identity building. In 2022, START Foundation expanded its reach by launching a digital learning platform, which faced challenges such as information overload and navigation difficulties due to a growing number of courses and a lack of individual support. To improve user experience on the digital learning platform, START Foundation explored implementing a recommender system for personalized course navigation based on methods from the field of artificial intelligence (AI). However, this exploration raised ethical concerns, particularly regarding bias and fairness in AI systems. In light of these concerns, the following questions emerged: Should START Foundation integrate the AI-based recommender system into its digital learning platform? If so, what factors should START Foundation consider regarding bias and fairness in its AI-based recommender system in order to prevent negative consequences for its scholars? Readers are encouraged to explore the interactive learning module on bias and fairness in AI systems, available at https://bias-and-fairness-in-ai-systems.de/en/home/.

Motion Planner With Fixed-Horizon Constrained Reinforcement Learning for Complex Autonomous Driving Scenarios

In autonomous driving, behavioral decision-making and trajectory planning remain huge challenges due to the large amount of uncertainty in environments and complex interaction relationships between the ego vehicle and other traffic participants. In this paper, we propose a novel fixed-horizon constrained reinforcement learning (RL) framework to solve decision-making and planning problems. Firstly, to introduce lane-level global navigation information into the lane state representation and avoid constant lane changes, we propose the constrained A-star algorithm, which can get the optimal path without constant lane changes. The optimality of the algorithm is also theoretically guaranteed. Then, to balance safety, comfort, and goal completion (reaching targets), we construct the planning problem as a constrained RL problem, in which the reward function is designed for goal completion, and two fixed-horizon constraints are developed for safety and comfort, respectively. Subsequently, a motion planning policy network (planner) with vectorized input is constructed. Finally, a dual ascent optimization method is proposed to train the planner network. With the advantage of being able to fully explore in the environment, the agent can learn an efficient decision-making and planning policy. In addition, benefiting from modeling the safety and comfort of the ego vehicle as constraints, the learned policy can guarantee the safety of the ego vehicle and achieve a good balance between goal completion and comfort. Experiments demonstrate that the proposed algorithm can achieve superior performance than existing rule-based, imitation learning-based, and typical RL-based methods.

УПРАВЛЕНИЕ ТРАЕКТОРИЕЙ ПОЛЕТА БЕСПИЛОТНОГО ЛЕТАТЕЛЬНОГО АППАРАТА ПРИ РАЗЛИЧНОЙ КОНФИГУРАЦИИ ИСТОЧНИКОВ НАВИГАЦИОННОЙ ИНФОРМАЦИИ

In real conditions of application for high-precision positioning and trajectory control of unmanned aerial vehicles (UAVs) when flying along a route, insufficient noise immunity and operating accuracy of satellite navigation system receivers are manifested. In this regard, it is relevant to study possible methods and means of providing high-precision navigation definitions based on complex processing of signals from various sources of navigation information when solving the problem of displaying a UAV in a terminal set. The article presents the results of developing a UAV trajectory control algorithm based on methods of statistical optimal control theory, the implementation of which will improve the accuracy of maintaining a given flight route. The characteristics for analyzing errors in maintaining the flight path are considered. The results of modeling and research of the characteristics of the trajectory control algorithm for various configurations of navigation information sources (NIS) are presented and the dependence of the accuracy of maintaining a given UAV flight route on errors in estimating navigation parameters is shown.

АНАЛИЗ ПРИНЦИПОВ ОБРАБОТКИ НАВИГАЦИОННОЙ ИНФОРМАЦИИ И ПОСТРОЕНИЯ РАБОЧЕЙ ЗОНЫ МНОГОПОЗИЦИОННОЙ СИСТЕМЫ НАБЛЮДЕНИЯ

The use of modern surveillance equipment in accordance with ICAO recommendations is aimed at improving the efficiency of air transportation, increasing the capacity of airspace and airfields, and improving the safety of flights and ground operations. The introduction in the Russian Federation of a multi-position surveillance system (MPSS) based on automatic dependent surveillance of the broadcast type (ADS-B) is intended to facilitate the implementation of state and regional aviation development programs in terms of creating conditions for improving flight safety, availability and quality of air navigation services for airspace users. Therefore, the analysis of the principles of constructing a working area and processing navigation information in a multi-position surveillance system for improving the accuracy of aircraft position-fixing is an urgent research task. An approach to improving the efficiency of the MPSS operation when processing information in conditions of noise and interference has been considered. Analysis of the results of modeling the proposed algorithm based on the discrete Kalman filter shows high accuracy of estimating the planned coordinates of an aircraft. Specialized software has been developed to automate the process of calculating and constructing working areas of a multi-position surveillance system.

Study of the influence of the parametric identifiability degree on the estimation accuracy of navigation system errors

The high-precision correction problem of navigation information for a small unmanned aerial vehicle flying with altitude changes over a wide range is studied. Numerical criteria of parametric identifiability degreefor one class of nonlinear systems represented by the SDC method are considered. Error models for inertial navigation system of various detail levels are studied and the results of mathematical and semi-natural modeling of a nonlinear Kalman filter with different models are presented. Keywords small unmanned aerial vehicle, inertial navigation system, errors, correction, degree of parametric identifiability, nonlinear Kalman filter, SDC method

How Canadian caregivers of persons living with dementia are managing in a fragmented system

People living with dementia (PLWD) often need many different health and social services throughout their journey, and navigating and accessing these services can be challenging due to a lack of integration in dementia care. PLWD often rely on caregivers, usually family members, to help them navigate the system and access the care and services they need. The importance of better co-ordination and integration of health, community, and residential dementia services has been highlighted in the World Health Organization’s (WHO) global action plan on the public health response to dementia and Canada’s National Dementia Strategy, however a widespread, robust plan for integrated care for PLWD has yet to be implemented. This leaves caregivers with the task of seeking out and accessing services on their own. The Alzheimer Societies across Canada aim to help caregivers navigate and access services by providing information, education, and support in system navigation to caregivers. In this study, we explored the experiences of caregivers accessing health and social services for their loved ones with dementia, and the role that the Alzheimer Society played in this journey. This work has been informed and guided by members the Alzheimer Society of Canada’s People with Lived Experience Advisory Group.
 We conducted semi-structured interviews with 34 caregiver clients of the Alzheimer Society across Canada in the Fall of 2022. Interviews were completed by phone or video conference and lasted between 20 and 73 minutes (mean=32 minutes). Participants were all caregivers of a PLWD and ranged in age from 42-89 (mean age= 63 years). There were 10 men, 23 women, and one non-binary person in our sample (caregiver spouse=18, caregiver child=15, caregiver sibling=1). Interviews were recorded and transcribed verbatim. Deductive coding, based on the interview questions, was used to identify themes.
 Experiences accessing health and social services varied widely among participants based on geographic location, disease progression or symptoms, existing social support and self-advocacy skills, and finances. Many caregivers had trouble accessing the services that they needed and found the system to be difficult to navigate. In many cases, the Alzheimer Society played an important role in making caregivers aware of the health and community services available and provided guidance on how to access those services in a timely way. Some participants, however, found that despite being provided with information and education, they continued to struggle due to a lack of access to quality care in the community (e.g. adult day programs, respite, and long-term care). Some participants experienced such extreme distress at being unable to access the care they needed for the PLWD, that they experienced physical and mental health issues themselves. 
 The Alzheimer Society and similar organizations help to make up for the lack of an integrated care system for PLWD, however, there remain large gaps in the ability of caregivers to access timely, adequate care. Fragmented, disjointed, and difficult to access care can have negative consequences for PLWD and their caregivers, and action is needed to further support the development of integrated dementia care services across Canada.

The co-development of a mobile navigation app in an integrated care network in Ontario, Canada

Introduction: The complexity of navigating health and social care services is a common challenge to both providers and patients and their families. In the nineties, navigation programs arouse to assist cancer patients accessing services. Since then, those programs expanded to additional patient populations. Navigation programs can take various forms including virtual navigation. Virtual navigation in healthcare is a proactive process by which patients obtain information and support via internet resources to manage their illness demands.
 Approach: With a commitment to facilitate 24/7 access to its attributed population, Burlington Ontario Health Team (BOHT)-an integrated care network in Ontario- developed a mobile navigation app. Developing this app utilized experienced-based co-design approach. This approach took place via organizing a working group that included all stakeholders within the BOHT network of organizations and patient/caregiver partners. Over several months, the working group built potential personas of users, mapped their system navigation needs, created an inventory of navigation services within the network and designed the navigation app. Creation of the app was an iterative process including several testing and feedback loops to improve content and usability.
 Results: The first iteration of Burlington Navigation app was launched in Fall of 2021 accompanied by a comprehensive communication plan. The plan included digital signals at the hospital and primary care offices, social media posts and presentations to local providers including primary care physicians, community navigators. The first iteration focused on linking to health, social and community services offered by BOHT’s members and collaborators, and also services that were widely used in the community. Based on the feedback we received afterwards, the second iteration included links to provincial services. The third iteration includes capability to create a trusted account verified in the background by the same technology Interac uses. This will allow secure and seamless access to integrated services without a need for a separate username and password as well as access to the provincial patient portal
 Implications/highlights: Burlington Navigation app was the first community-based navigator app co-designed with patient partners, providers and community volunteers with lived experiences within the health and social care systems in Ontario. It resonated well with the users (providers and patients/caregivers) as local information became available at their fingertips. The success of the app allowed it to guide other OHTs on a similar digital navigator design journey. It also became the blueprint for a provincial solution for a multi-tenant navigator, which would allow multiple OHTs to showcase the local navigation information (based on geolocation) as well as provincial offerings.
 Conclusion: Sharing our journey co-developing the Burlington navigation app demonstrates that when local health integrated care networks utilize experienced-based co-design approaches to planning healthcare interventions, this has the potential to a) develop solutions that are well perceived by patients and providers and b) scale and spread these local solutions to larger-scale solutions that can improve the experiences of a wider spectrum of providers and patients.

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