Navigation Approach Research Articles

Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: an ex-vivo feasibility study

BackgroundWith the increasing number of surgeries utilizing spinal instrumentation, three-dimensional surgical navigation aims to improve the accuracy of implant placement. However, its widespread clinical adaption has been hindered by factors such as high radiation exposure and interference with standard surgical workflows.MethodsX23D is a novel AI-based fluoroscopy reconstruction technique that generates a 3D anatomical model of the spine from only four fluoroscopy images. Based on this technology, we developed a prototype for the surgical navigation of pedicle screws placement of the lumbar spine, visualizing the 3D-reconstructed spine anatomy and the surgical drill position in real-time. An ex-vivo study was conducted to compare the accuracy of the X23D-based navigation approach with fluoroscopy-aided freehand instrumentation. Five board-certified surgeons placed pedicle screws on six human torsi within a realistic surgical environment. Breach rate, site and extent (Gertzbein-Robbins) were evaluated in postoperative CT scans, as well as execution time, radiation dose, and user experience. Specimens, operating side, and surgeon were randomised.ResultsForty-nine pedicle screws (n = 24 × 23D, n = 25 2D-fluoroscopy) were evaluated, with six breaches occurring in the control group, one of which was considered clinically significant (medial breach grade C). Five breaches with one clinically significant breach were observed in the X23D group. Breach rate, execution time for each lumbar level (X23D 167 s vs. control 156 s), radiation dose (X23D 33.26 mGy vs. control 49.5 mGy), and user experience did not reveal significant differences (p > 0.05) between the groups.ConclusionsSpinal navigation using the X23D-based approach shows promise and performs well in a realistic surgical ex-vivo setting. With further refinements, its accuracy is expected to match clinical-grade navigation systems while reducing radiation dose.

A Hybrid BFO/PSO Navigation Approach for an Autonomous Mobile Robot

A Hybrid BFO/PSO Navigation Approach for an Autonomous Mobile Robot

Multi-type feature fusion visual navigation for asteroid landing

In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features, such as significant recognition and extraction errors, low computational efficiency, and limited navigation accuracy, a novel approach for multi-type fusion visual navigation is proposed. This method aims to overcome the limitations of single-type features and enhance navigation accuracy. Analytical criteria for selecting multi-type features are introduced, which simultaneously improve computational efficiency and system navigation accuracy. Concerning pose estimation, both absolute and relative pose estimation methods based on multi-type feature fusion are proposed, and multi-type feature normalization is established, which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation. The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS’s superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

Social robot navigation through constrained optimization: A comprehensive study of uncertainty-based objectives and constraints in the simulated and real world

This paper provides an empirical evaluation, in both simulation and real scenarios, of the social navigation problem when considering human motion prediction and its stochastic effects. To this end, we study several different optimization criteria and constraints related to the uncertainty of predicting pedestrians’ motion, embedded into the Model Predictive Control (MPC) scheme.The main research question of this work is the following: what are the most important uncertainty-based criteria for the social MPC both in simulated and real-world environments? In order to achieve a solid answer to this question, we extend the results previously obtained from our work (Akhtyamov et al., 2023) in the simulated environments and provide a real-world setting that mimics similar conditions, for a fair comparison of the qualitative and quantitative results.The main conclusions supported by both of the evaluation environments are the advantages of using adaptive constraints as a clear undisputed enhancement and the problems raised when considering uncertainty-aware criteria. We hope this paper is of interest to the community for deciding and designing uncertainty-aware approaches for social robot navigation.

Технологии и методы планирования перемещения БПЛА по маршрутным точкам

Unmanned aerial vehicles (UAVs) occupy a special place in our world. Their ability to navigate along set routes opens up prospects in various fields. The purpose of the study is to review and analyze navigation systems and UAV routing algorithms, methods that allow UAVs to follow the route with high accuracy. GPS and inertial navigation (INS) systems that provide accurate location determination are being investigated. The capabilities of sensor systems — cameras, lidars and ultrasonic sensors — are analyzed to detect obstacles and adjust the trajectory; voxel maps for three-dimensional modeling of the environment and methods of simultaneous localization and mapping (SLAM); algorithm A* (A-star); genetic routing algorithm, potential-based obstacle avoidance algorithms and RRT. Practical significance: the use of these methods and technologies can significantly improve the safety and accuracy of UAV routing, the ability to navigate autonomously in complex and dynamically changing landscapes. In conclusion, the advantages and limitations of navigation approaches and technologies are discussed; the importance of integrating sensor systems and SLAM methods to increase the autonomy and efficiency of UAVs; directions for further research.

A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach

The paper presents a Fuzzy Q-Learning (FQL) and optical flow based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.

Multi-agent long-distance end-to-end indoor navigation: using imitation learning pre-training and global map

Abstract To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments.

Abstract A158: Community-based lung cancer screening navigation for populations at high risk

Abstract Rationale: Lung cancer screening (LCS) can reduce mortality in individuals with a high-risk smoking history, but uptake has been uneven and suboptimal. In our catchment area of Washington state, urban American Indian/Alaska Natives (AI/AN) and sexual and gender minorities (SGM) have high rates of commercial tobacco use and lower uptake of LCS. Mixed-methods community-engaged formative research with both populations revealed barriers to LCS related to social determinants of health, and interest in identity-tailored approaches to LCS care. We used this to develop tailored navigation approaches to LCS. Our study objective was to pilot and evaluate patient navigation programs to facilitate LCS uptake and care completion for these community members. Methods: We conducted parallel LCS navigation pilot studies, with participants who self-identified as AI/AN or as a member of SGM communities and who were LCS screen eligible. Participants were recruited through referrals to our multi-site university-based LCS program, community-based organization partnerships, outreach, and social media. Dedicated patient navigators were trained on LCS processes, navigation, and community-specific best practices. Navigation was guided by handbooks with barrier-specific resources, standardized scripting for contact points, and community-based resources specific to the populations. Participants completed pre- and post-navigation (1-3 months after enrollment) surveys and were followed for LCS uptake, outcomes, and navigation activities through chart review. Results: Between July 1, 2023 and February 1, 2024, 40 participants were enrolled in the navigation pilot studies, 21 in the AI/AN arm and 19 in the SGM arm. The median age of participants was 65 (interquartile range (IQR): 59-69), 80% reported current smoking, and 65% reported a household income below $30,000. Most (55%) reported at least one barrier that delayed access to healthcare in the past year. After enrollment, 35% completed LCS within one month and 55% completed LCS to date. Participants who completed post-surveys (n=22, 55%) indicated that the patient navigator was helpful. Common navigation activities included scheduling assistance, understanding screening results, addressing financial issues, and smoking cessation. Overall, participants approved of navigation approaches with a median Acceptability of Intervention Measure score of 5/5 (IQR: 4.8-5) and a median Patient Satisfaction with Interpersonal Relationship with Navigator score of 44.5/45 (IQR: 39.3-45). Conclusion: These single-arm pilot trials suggest tailored navigation for LCS for AI/AN and SGM communities is a useful approach to engage patients and address barriers and appears acceptable and welcomed. However, substantial variation in LCS completion may reflect complex barriers not addressed through navigation. Community-based and tailored navigation offers a promising approach to address LCS disparities, though further bi-directional refinement of patient navigation is necessary to optimize engagement and LCS completion. Citation Format: Ursula Tsosie, Katie DeCell, Nicolas Anderson, Madison Snidarich, Brandon Omernik, Nicholas Murphy, Matthew Triplette. Community-based lung cancer screening navigation for populations at high risk [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A158.

A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles

Collision-avoidance path planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments presents a significant challenge, particularly considering the maneuverability limitations of fin-rudder-controlled AUVs. This study proposes a novel navigation approach that combines the strengths of Reciprocal Velocity Obstacle (RVO) principles and Model Predictive Path Integral (MPPI) control to enhance autonomous obstacle avoidance for multiple AUVs. The key innovation lies in leveraging RVO-based obstacle avoidance velocities to enhance both action sampling and cost function optimization in MPPI framework. Specifically, a neural network trained through supervised learning is utilized to convert the RVO-provided avoidance velocities into desired actions for MPPI. Additionally, these velocities are incorporated into the MPPI cost function, thereby enhancing the optimization process and facilitating convergence towards optimal, collision-free navigation solutions. Simulation experiments validated the method's capability to provide effective obstacle avoidance behavior across various complex scenarios. Results indicate that this approach successfully integrates MPPI's predictive capabilities with RVO's velocity avoidance logic, enabling AUVs to navigate efficiently through complex underwater environments while ensuring mission safety and effectiveness. This approach offers a promising new solution for autonomous navigation of AUVs, with potential applications in ocean exploration, underwater search and rescue, and other maritime operations. Future research will explore the performance of this approach in real-world underwater environments and consider the impact of additional dynamic factors.

A two-context-aware approach for navigation: A case study for vehicular route recommendation

In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.

Family member and service provider experiences and perspectives of a digital surveillance and service navigation approach in multicultural context: a qualitative study in identifying the barriers and enablers to Watch Me Grow-Electronic (WMG-E) program with a culturally diverse community

BackgroundChildren and families from priority populations experienced significant psychosocial and mental health issues to the COVID-19 pandemic. Yet they also faced significant barriers to service access, particularly families from culturally and linguistically diverse (CALD) backgrounds. With most child and family health nurse clinics ceasing in-person consultations due to the pandemic, many children missed out on health and developmental checks. The aim of this study was to investigate the perspectives and experiences of family members and service providers from an urban, CALD community regarding the implementation of a digital, developmental surveillance, Watch Me Grow-Electronic (WMG-E) program.MethodsSemi-structured interviews were conducted with 17 family members, service navigators, and service providers in a multicultural community in South Western Sydney, Australia. This qualitative study is an implementation evaluation which formed as part of a larger, two-site, randomised controlled trial of the WMG-E program. A reflexive thematic analysis approach, using inductive coding, was adopted to analyse the data.ResultsParticipants highlighted the comprehensive and personalised support offered by existing child and family health services. The WMG-E was deemed beneficial because the weblink was easy and quick to use and it enabled access to a service navigator who support family access to relevant services. However, the WMG-E was problematic because of technology or language barriers, and it did not facilitate immediate clinician involvement when families completed the weblink.ConclusionsFamilies and service providers in this qualitative study found that using WMG-E empowered parents and caregivers to access developmental screening and learn more about their child’s development and engage with relevant services. This beds down a new and innovative solution to the current service delivery gap and create mechanisms that can engage families currently not accessing services, and increases knowledge around navigating the health and social care services. Notwithstanding the issues that were raised by families and service providers, which include accessibility challenges for CALD communities, absence of clinical oversight during screening, and narrow scope of engagement with available services being offered, it is worth noting that improvements regarding these implementation factors must be considered and addressed in order to have longevity and sustainability of the program.Trial registration.The study is part of a large randomised controlled trial (Protocol No. 1.0, Version 3.1) was registered with ANZCTR (registration number: ACTRN12621000766819) on July 21st, 2021 and reporting of the trial results will be according to recommendations in the CONSORT Statement.

A Switched Approach for Smartphone-Based Pedestrian Navigation.

In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path.

A bronchoscopic navigation method based on neural radiation fields.

We introduce a novel approach for bronchoscopic navigation that leverages neural radiance fields (NeRF) to passively locate the endoscope solely from bronchoscopic images. This approach aims to overcome the limitations and challenges of current bronchoscopic navigation tools that rely on external infrastructures or require active adjustment of the bronchoscope. To address the challenges, we leverage NeRF for bronchoscopic navigation, enabling passive endoscope localization from bronchoscopic images. We develop a two-stage pipeline: offline training using preoperative data and online passive pose estimation during surgery. To enhance performance, we employ Anderson acceleration and incorporate semantic appearance transfer to deal with the sim-to-real gap between training and inference stages. We assessed the viability of our approach by conducting tests on virtual bronchscopic images and a physical phantom against the SLAM-based methods. The average rotation error in our virtual dataset is about 3.18 and the translation error is around 4.95 mm. On the physical phantom test, the average rotation and translation error are approximately 5.14 and 13.12 mm. Our NeRF-based bronchoscopic navigation method eliminates reliance on external infrastructures and active adjustments, offering promising advancements in bronchoscopic navigation. Experimental validation on simulation and real-world phantom models demonstrates its efficacy in addressing challenges like low texture and challenging lighting conditions.

An Autonomous Cooperative Navigation Approach for Multiple Unmanned Ground Vehicles in a Variable Communication Environment

An Autonomous Cooperative Navigation Approach for Multiple Unmanned Ground Vehicles in a Variable Communication Environment

Impact of a first-year medical student patient navigation initiative on healthcare access and students' skills

Background: Medical schools with complex patient populations, characterized by linguistic diversity and uncertain immigration status, face challenges in imparting essential skills in patient care, particularly in understanding healthcare barriers, building rapport, and fostering empathy. A South Florida medical school with a predominantly non-English-speaking and undocumented patient population piloted a Patient Navigation (PN) program within the first-year curriculum to address these challenges. We present the student feedback from the program’s first two years to guide medical schools in simultaneously caring for this unique patient population, with the aim of enhancing medical education. Methods: This program diverged from traditional Patient Navigation approaches by leveraging specialized hospital-associated translation services and collaborating with the Health System's Financial Assistance program. We performed a crosssectional study on student feedback surveys to identify program features significantly associated with a satisfying PN experience, and to provide insights to optimize and refine the program for future implementations. Results: The program engaged 245 out of 306 first-year medical students in PN for 118 patients. Successful contact was established with 55% of patients and 70% achieved their navigation objectives. Satisfaction metrics revealed that students given patients were nearly five times more satisfied than their counterparts. This positively impacts both student experience and patient outcomes, showcasing the program's effectiveness in bridging healthcare disparities. Discussion: The PN program proved successful in linking low-risk patients to follow-up care, offering medical students first-hand exposure to the challenges of care for primarily non-English-speaking and undocumented populations. The initiative aimed to enhance healthcare access for marginalized patients while providing medical students with an immersive understanding of patient-centric care beyond conventional curricular offerings. Key lessons learned include improving communication channels between students and patients, and optimizing community resources for streamlined patient support. Future iterations will prioritize these lessons, emphasizing cultural competence and patient advocacy to enhance both student education and patient care outcomes.

Validation of Spanish-Language Surveys Utilized for the Navigator-Assisted Hypofractionation (NAVAH) Program to Aid Hispanic-American Breast Cancer Patients.

Cancer accounts for 22% of all mortality and is the leading cause of death among Hispanic and/or Latinx patients in the United States. The disparities in access to radiation therapy (RT), mortality rates, and treatment outcomes among Hispanic-American breast cancer patients compared with other populations highlight the urgent need for targeted interventions. The Navigator-Assisted Hypofractionation (NAVAH) program, with its innovative patient navigation approach and culturally sensitive survey, aims to better identify the specific barriers faced by this population. This study is a report of the NAVAH program experience piloting a Spanish-language culturally sensitive survey in Hispanic-American volunteers. Hispanic-American volunteers with fluency in Spanish were recruited to participate in survey conduction, identified from local networks. Survey information was assessed by topic category, and survey responses were amalgamated into a representative score for each category. Survey categories include acceptability (comfort and prejudice among interactions with the system), accessibility (transportation, distance to care, and health care literacy), accommodation (access to the internet, navigating transportation), affordability (financial considerations, employment, and level of education), and availability (access to a medical center, coordinating care, and overall quality of care). A total of 6 volunteers meeting inclusion criteria completed the survey; 4 in person and 2 by telephone. The median survey completion time was 12 minutes 38 seconds. Respondents noted satisfaction and trust in their interactions with medical providers; however, responses in the acceptability category highlighted a high perception of disparities in the medical system, including a high prevalence of racial and ethnic prejudice and a high prevalence of treatment differences between high-income and low-income patients in clinical settings. In the first Spanish-language survey of its kind, our findings indicate that this survey design is feasible in the Hispanic-American population. Implementation of this survey in breast cancer patients will provide more definitive and comprehensive answers regarding other categories in the survey, including financial challenges during treatment, access to accommodations, and perception of treatment during cancer care. The investigation involving patients actively receiving breast cancer RT is currently underway.

Conceptualizing the social networks of children of parents with serious mental illness: a thematic analysis.

Social networks, defined as the set of active and significant ties surrounding an individual, influence the wellbeing of vulnerable children. The best evidenced mechanism through which this occurs is where networks act as a vehicle to access social support. Little is known about the content and function of social networks of children of parents with severe and enduring mental illness (COPMI). COPMI are a frequently under-identified vulnerable child population at risk of negative outcomes. This qualitative study investigates the structure, role and function of these children's networks. Researchers conducted 17 semi-structured egocentric social network interviews. Interviews incorporated personal network mapping as a data collection method. COPMI were recruited through third sector organizations and interviewed across three sites in England. Data was analyzed using an inductive thematic analysis. Five network features were identified (i) parents as primary providers of support (i) limited networks and diminished connections over time (iii) substitutable ties (formal and informal) (iv) peer connections as source of both support and strain (v) coping strategies: self-censorship, avoidance and animals. Children of parents with severe and enduring mental illness networks are structurally typical of vulnerable children in that they are limited, rely on parents as primary ties but allow for some substitution of support ties. COPMI-specific features included peer relationships at times as source of strain and network level coping strategies used to manage wellbeing, including pets. This latter reflects previous findings in vulnerable adult populations so far unevidenced in children. Little evidence as to the mechanistic effect at work within networks was collected. However, COPMI were clearly shown to be engaged in active management and strategising in network navigation approaches, indicating the need to engage with children in this capacity, rather than approaching them as passive recipients of support. As such, effective network level interventions for this group are likely to prioritize access to beneficial substitute ties when support is limited. Additionally, interventions that promote network navigation skills and help foster productive coping strategies can capitalize on the child's active management role within their network.

Promoting a Culture of Care for College Students with Marginalized Identities Using an Embedded Prevention Navigation Model

ABSTRACT College students are at risk for mental and physical health concerns such as depression, anxiety, substance use, HIV, and other sexually transmitted infections, creating greater demands for timely and responsive services. However, students from marginalized groups often face greater challenges accessing physical and behavioral health services due to historic and systemic barriers. One unique and economical approach to helping students access care is to train peers to provide psychoeducation and timely and responsive linkages to behavioral and physical health resources and embed them in spaces where students naturally congregate. This approach has proven effective in reducing engagement in high-risk behaviors and encouraging resource utilization in community settings, but little research exists on using an embedded prevention navigator approach in college settings. The present study describes the development, implementation, and evaluation of such an approach piloted among 88 undergraduates, the majority of whom identified as students of color and first-generation college students. Findings support the feasibility of using a peer-delivered prevention navigation approach to provide students with mental health, substance use, and sexual health education and referrals to relevant resources. Lessons learned and recommendations for college mental health professionals are discussed.

Navigation performance analysis of Earth–Moon spacecraft using GNSS, INS, and star tracker

Global Navigation Satellite System (GNSS) can provide an approach for spacecraft autonomous navigation in earth–moon space to make up for the insufficiency of earth-based tracking, telemetry, and control systems. However, its weak power and poor observation geometry near the moon causes new problems. After the GNSS signal characteristics and satellite visibility were evaluated in Phasing Orbit and Lunar Transfer Orbit, we proposed an adaptive Kalman filter based on the Carrier-to-Noise ratio (C/N0) and innovation vector to weaken the influence of GNSS accuracy attenuation as much as possible. The experimental results show that the spacecraft position and velocity accuracy are better than 10 m and 0.1 m/s near the Earth, and better than 50 m and approximately 0.2 m/s near the moon use GNSS with the proposed adaptive algorithms. Additionally, because of the deterioration of navigation performance based on the orbit filter during orbital maneuvering, we used accelerometer data to compensate for the dynamic model to maintain navigation performance. The results of the experiment provide a reference for subsequent studies.