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The Double Asteroid Redirection Test (DART) mission is NASA's first planetary defense mission to demonstrate the viability of kinetically impacting an asteroid and deflecting its trajectory. The DART spacecraft successfully launched on November 24, 2021 from the Vandenberg Space Force Base and successfully made impact on Dimorphos, the smaller asteroid in the Didymos system, on September 26, 2022. The DART spacecraft has one instrument called Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO). DRACO is an imaging telescope that, in conjunction with the SMART Navigation algorithm, autonomously guided the DART spacecraft to the asteroid. Because DRACO is a mission critical and light sensitive instrument, the DRACO Door mechanism was designed as the protective cover. The door functions to shield DRACO from stray light during launch, to deploy in space once when commanded, and to stay 180 degrees open for the duration of the mission. The DRACO Door went through several iterations during the design phase with decisions on various components such as Frangibolts ®, torsion springs, hardstops, and latches. After fabrication and assembly, the door went through a rigorous environmental testing plan, which included deployment testing, vibration testing, and thermal vacuum testing. After successful qualification of the mechanism, the door was installed and integrated into the DART spacecraft. It should be noted that during the fabrication of the mechanism piece-parts, the COVID-19 pandemic began, and the effects of the pandemic were seen in the challenges faced during the DRACO door assembly and testing. Under the constraints of the pandemic, the DART spacecraft was successfully built, tested, and launched, and the DRACO door was successfully deployed on December 7, 2021. The door has continued to function as intended. This paper will discuss the design choices behind the door components, the environmental qualification test program, and the installation of the door onto the DART spacecraft. In addition, this paper will discuss the lessons learned and the challenges of fabricating and testing the flight hardware. © 2023 IEEE.
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The novel coronavirus (COVID-19) outbreak has impacted the aviation industry worldwide. Several restrictions and regulations have been implemented to prevent the virus's spread and maintain airport operations. To recover the trustworthiness of air travelers in the new normality, improving airport service quality (ASQ) is necessary, ultimately increasing passenger satisfaction in airports. This research focuses on the relationship between passenger satisfaction and the ASQ dimensions of airports in Thailand. A three-stage analysis model was conducted by integrating structural equation modeling, Bayesian networks, and artificial neural networks to identify critical ASQ dimensions that highly impact overall satisfaction. The findings reveal that airport facilities, wayfinding, and security are three dominant dimensions influencing overall passenger satisfaction. This insight could help airport managers and operators recover passenger satisfaction, increase trustworthiness, and maintain the efficiency of the airports in not only this severe crisis but also in the new normality.
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In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.
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In this paper, we propose a path control framework for guiding and simulating the patient's path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.
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In recent years, the novel corona virus pandemic is raging around the world, and the safety of home environment and public environment has become the focus of people's attention [2]. Therefore, the research on disinfection robot has become one of the important directions in the field of machinery and artificial intelligence. This paper proposes a robot with the STM32 MCU as the core of disinfection, and is equipped with a variety of sensors and a camera vision, has the original cloud service management platform, the remote deployment of navigation, based on visual SLAM to realize high precision navigation and positioning, can realize to indoor environment autonomously route planning, automatic obstacle avoidance checking, disinfection, epidemic prevention function, at the same time can pass Bit computer software realizes remote control of robot, which has great development potential. © 2022 ACM.
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With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.
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This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.
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There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.
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BACKGROUND: There is an urgent need for evidence on how interventions can prevent or mitigate cancer-related financial hardship. Our objectives are to compare self-reported financial hardship, quality of life, and health services use between patients receiving a financial navigation intervention versus a comparison group at 12 months follow-up, and to assess patient-level factors associated with dose received of a financial navigation intervention. METHODS: The Cancer Financial Experience (CAFÉ) study is a multi-site randomized controlled trial (RCT) with individual-level randomization. Participants will be offered either brief (one financial navigation cycle, Arm 2) or extended (three financial navigation cycles, Arm 3) financial navigation. The intervention period for both Arms 2 and 3 is 6 months. The comparison group (Arm 1) will receive enhanced usual care. The setting for the CAFÉ study is the medical oncology and radiation oncology clinics at two integrated health systems in the Pacific Northwest. Inclusion criteria includes age 18 or older with a recent cancer diagnosis and visit to a study clinic as identified through administrative data. Outcomes will be assessed at 12-month follow-up. Primary outcomes are self-reported financial distress and health-related quality of life. Secondary outcomes are delayed or foregone care; receipt of medical financial assistance; and account delinquency. A mixed methods exploratory analysis will investigate factors associated with total intervention dose received. DISCUSSION: The CAFÉ study will provide much-needed early trial evidence on the impact of financial navigation in reducing cancer-related financial hardship. It is theory-informed, clinic-based, aligned with patient preferences, and has been developed following preliminary qualitative studies and stakeholder input. By design, it will provide prospective evidence on the potential benefits of financial navigation on patient-relevant cancer outcomes. The CAFÉ trial's strengths include its broad inclusion criteria, its equity-focused sampling plan, its novel intervention developed in partnership with clinical and operations stakeholders, and mixed methods secondary analyses related to intervention dose offered and dose received. The resulting analytic dataset will allow for rich mixed methods analysis and provide critical information related to implementation of the intervention should it prove effective. TRIAL REGISTRATION: ClinicalTrials.gov NCT05018000 . August 23, 2021.
Subject(s)
Financial Stress , Neoplasms , Adolescent , Humans , Neoplasms/diagnosis , Quality of Life , Treatment OutcomeABSTRACT
BACKGROUND: Historically, people aged 65 years or older have been slower to adopt new technol-ogy. However, technology use in this demographic continues to increase. OBJECTIVES: This study aimed to understand how patients with cancer who are aged 65 years or older engage with technology and whether patient behavior related to technology use has changed because of the COVID-19 pandemic. In addition, this study evaluated whether respondents' understand-ing of technology was associated with increased likelihood of adoption and perceived utility of the ONS On-CallTM cancer treatment symptom assessment tool. METHODS: A U.S. population-based anonymous online survey was conducted between May 17 and May 31, 2021, with 103 patients with cancer aged at least 65 years. FINDINGS:The majority of respondents used tech-nology regularly as part of their daily lives. Activities included shopping online, reading the news, or engaging with a healthcare platform. As a result of the COVID-19 pandemic, most respondents reported an increased use of digital activities, particularly the use of healthcare technology. Respondents reported they would be likely to use ONS On-Call, particularly if it is recommended by a healthcare provider.
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The coronavirus (COVID-19) pandemic has impacted breast cancer screening with concerns that this may lead to increased overall breast cancer mortality and worsened racial and ethnic disparities in breast cancer survival. As pandemic recovery efforts are underway, we must be prepared to address barriers to timely access of breast imaging services, including those that existed prior to the pandemic, as well as any new barriers that may arise as a result of the pandemic. Patient navigation is an important tool that has been shown to address barriers to timely breast imaging access and help reduce disparities. Patient navigation programs can serve as a key part of the strategy to mitigate the impact of the COVID-19 pandemic on timely breast cancer diagnosis. These programs have been shown to be successful in promoting adherence to breast cancer screening guidelines as well as encouraging timely diagnostic follow-up, particularly in underserved communities. Further research is needed to explore the role of using a telehealth platform for patient navigation and evaluate the cost-effectiveness of patient navigator programs as well as more randomized controlled trials to further explore the impact of patient navigation programs.
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Physical distancing is a key measure to slow the spread of many highly infectious diseases, e.g. COVID-19. Streetscape interventions such as pedestrian signage can contribute to ensuring distances are kept, but it is unclear to what extent people comply with these in practice. This paper tackles this question using an immersive video environment to realistically simulate real-life streetscapes in the lab. In a controlled user study, we augmented panoramic video footage with pedestrian one-way street signage and recorded route decisions to assess compliance with distance keeping measures. Our results indicate that such signage affects routing decisions and can thus help pedestrians to avoid crowded situations where distance keeping is difficult. We also identified further factors affecting decisions and a correlation between intention to comply and actual compliance. The experimental method we used enabled us to effectively and safely carry out a study of a phenomenon that in the real world depends on interaction with the physical environment. This method may have applications in other areas in which simulations of physical environments are important.
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INTRODUCTION: Patients frequently use the Web to obtain health information. This trend increased during the COVID19 pandemic. We aimed to assess the quality of Web-based information on robot-assisted radical cystectomy. METHODS: A Web search was conducted in November 2021 using the 3 most common engines (Google/Bing/Yahoo). Search terms were "robotic cystectomy," "robot-assisted cystectomy," and "robotic radical cystectomy." The top 25 results generated for each term by each search engine were included. Duplicate pages, pages advertised, and pages with paywall access were excluded. Selected websites were classified as academic, physician, commercial, and unspecified. The quality of site contents was evaluated using the DISCERN and Journal of the American Medical Association (JAMA) assessment instruments, and HONcode (Health on the Net Foundation) seal and reference presence. Flesch Reading Ease Score was used for readability assessment. RESULTS: Of the 225 sites examined only 34 were eligible for analysis, including 35.3% classified as "academic," 44.1% "physician," 11.8% "commercial," and 8.8% "unspecified." Average±SD DISCERN and JAMA scores were 45.5±15.7 and 1.9±1.1, respectively. Commercial websites had the highest DISCERN and JAMA scores with a mean±SD of 64.7±8.7 and 3.6±0.5, respectively. Physician websites had a significantly lower JAMA mean score than commercial ones (p <0.001). Six websites had HONcode seals and 10 reported references. Readability was difficult as it was at the level of college graduate. CONCLUSIONS: As the role of robot-assisted radical cystectomy continues to grow worldwide, the overall quality of Web-based information related to this procedure remains poor. An effort should be made by health care providers to assure patients can have better access to reliable and readable informational material.
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Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.
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Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed;the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot's velocity based on the following inputs;the patient's gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.
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The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author
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Over the years, the robotic industry has made significant growth in the manufacturing sector due to the need for collaborative and interactive robots. But it is not the case for service sectors, especially in the healthcare sector. A lack of emphasis is given to healthcare which has led to new opportunities for developing assistive robots which can aid patients with disabilities and illnesses. Furthermore, COVID-19 has acted as a catalyst for the development of assistive robots in the healthcare sector in an attempt to overcome the difficulties faced due to viruses and bacteria. This paper demonstrates the simulation of a multi-purpose medical assistive robot using ROS(Robot Operating System). This intelligent robot is successfully simulated and visualized in the ROS environment. To achieve real-Time autonomous motion Google Cartographer SLAM(Simultaneous Localization And Mapping) is used to generate real-Time maps of unknown environments. It usually focuses on how these robots can provide assistance to health workers, customers, and organizations in different sectors of the healthcare environment. © 2023 IEEE.
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Data visualization has become an increasingly important means of effective data communication and has played a vital role in broadcasting the progression of COVID-19. Accessible data representations, however, have lagged behind, leaving areas of information out of reach for many blind and visually impaired (BVI) users. In this work, we sought to understand (1) the accessibility of current implementations of visualizations on the web;(2) BVI users' preferences and current experiences when accessing data-driven media;(3) how accessible data representations on the web address these users' access needs and help them navigate, interpret, and gain insights from the data;and (4) the practical challenges that limit BVI users' access and use of data representations. To answer these questions, we conducted a mixed-methods study consisting of an accessibility audit of 87 data visualizations on the web to identify accessibility issues, an online survey of 127 screen reader users to understand lived experiences and preferences, and a remote contextual inquiry with 12 of the survey respondents to observe how they navigate, interpret, and gain insights from accessible data representations. Our observations during this critical period of time provide an understanding of the widespread accessibility issues encountered across online data visualizations, the impact that data accessibility inequities have on the BVI community, the ways screen reader users sought access to data-driven information and made use of online visualizations to form insights, and the pressing need to make larger strides towards improving data literacy, building confidence, and enriching methods of access. Based on our findings, we provide recommendations for researchers and practitioners to broaden data accessibility on the web. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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Competitions have been a regular feature at almost every ICRA and IROS conference. So far, the organization of competitions has been entirely up to the conference organizing committee (OC). The OC usually issues a call for competition proposals;collects them;and decides which competitions will be hosted based on the conference site and budget conditions. Many big companies, such as Amazon, Airbus, and DJI, have successfully organized competitions at ICRA and IROS, some of which have continued for multiple years and have driven technological advances in robotics. The OC may also organize competitions based on the interests of its financial supporters. Many such competitions have been one-time events, but the Autonomous Drone Racing competition, which ran for four years until it was interrupted by the COVID-19 pandemic, was a successful testbed for vision-based autonomous navigation of quadcopters and attracted participation from many top-tier research teams.
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Due to the global COVID-19 pandemic, there is a strong demand for pharyngeal swab sampling and nucleic acid testing. Research has shown that the positive rate of nasopharyngeal swabs is higher than that of oropharyngeal swabs. However, because of the high complexity and visual obscuring of the interior nasal cavity, it is impossible to obtain the sampling path information directly from the conventional imaging principle. Through the combination of anatomical geometry and spatial visual features, in this paper, we present a new approach to generate nasopharyngeal swabs sampling path. Firstly, this paper adopts an RGB-D camera to identify and locate the subject's facial landmarks. Secondly, the mid-sagittal plane of the subject's head is fitted according to these landmarks. At last, the path of the nasopharyngeal swab movement in the nasal cavity is determined by anatomical geometry features of the nose. In order to verify the validity of the method, the location accuracy of the facial landmarks and the fitting accuracy of mid-sagittal plane of the head are verified. Experiments demonstrate that this method provides a feasible solution with high efficiency, safety and accuracy. Besides, it can solve the problem that the nasopharyngeal robot cannot generate path based on traditional imaging principles. It also provides a key method for automatic and intelligent sampling of nasopharyngeal swabs, and it is of great clinical value to reduce the risk of cross-infection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.