Robotic Technology Research Articles

Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

Background: Patient engagement refers to the actively involvement of individuals in their own treatment, decision-making, and partnering with healthcare providers. Due to the progress of Artificial Intelligence (AI), there is a groundbreaking shift happening in healthcare that is bringing numerous benefits to patients and health systems. AI technologies are not only used as instruments in healthcare but also evolving into collaborators for diagnosing and treating patients, as well as enhancing the quality of services, offering a personalized, timely, and interactive healthcare experience. This article delves into how AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements. Methods: The research made use of previously published materials on artificial intelligence, data, and robotic technologies in healthcare settings to explore ways in which patients can be effectively involved in the advancement of these technologies in healthcare settings. Results: AI-powered decision support systems improve healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence. AI enables proactive healthcare interventions by detecting potential health issues in real time, allowing for remote patient monitoring. Motivating patients to actively participate can result in improved adherence to the treatment plan. Overall, patient engagement appears to be the most well-developed and progressive concept for enabling patients to participate actively in their healthcare. Conclusions: AI technologies are more than just tools in healthcare; they are evolving into collaborators in patient involvement, providing a personalized, proactive and engaging healthcare journey. Further evidence is required to comprehend how patients engage in the process and if this leads to better quality of care. In the dynamic health sector, change management is essential for continuously updating and adjusting healthcare facilities to address the evolving needs of patients. Health systems that do not adjust to changes in a timely manner are unsuccessful.

Hydrogel-based Soft Robotics for Surgical Machinery

Hydrogel-based soft robotics represent a transformative approach in surgical machinery, offering unparalleled adaptability, biocompatibility, and precision for minimally invasive procedures. Hydrogels, with their high water content and tunable mechanical properties, mimic soft biological tissues, making them ideal for applications in delicate surgical environments. This research explores the integration of hydrogel materials into soft robotic systems, focusing on their fabrication, actuation mechanisms, and performance in surgical applications. Key advancements include the development of stimuli-responsive hydrogels that enable precise control of movement and force, enhancing the capability to navigate complex anatomical structures. The study also examines computational models for simulating hydrogel behavior and optimizing robotic designs. While the potential benefits of hydrogel-based soft robotics in improving patient outcomes are significant, challenges remain in ensuring durability, scalability, and reliable control systems. This research aims to address these limitations by integrating advanced materials science with robotic engineering. By doing so, it contributes to the evolution of surgical technologies, paving the way for safer and more effective procedures.

Multimodal fusion-powered English speaking robot

IntroductionSpeech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.MethodsTo overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.Results and discussionIn the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.

Comparison of outcomes between single-port and multi-port robotic radical nephrectomy.

Single-port (SP) robotic surgery is a novel technology, and although there is emerging data, it remains limited in assessing single-port (SP) robot-assisted surgery as an alternative to multi-port (MP) platforms. To compare perioperative and postoperative outcomes between SP and MP robotic technologies for radical nephrectomy (RN). This is a retrospective cohort study of patients who have undergone robot-assisted radical nephrectomy using either the SP or MP technology. Baseline demographics, clinical, tumor-specific characteristics, and perioperative and postoperative outcomes were compared using χ2, t-test, Fisher exact test, and Mann-Whitney U test. Multivariable analyses were conducted using robust, Poisson, and logistic regressions. A total of 341 patients underwent robotic RN with 47 patients (14%) in the SP group. The two groups exhibited similar baseline characteristics, with no significant differences in age, sex, body mass index, Charlson comorbidities index, and tumor laterality. However, SP group had a smaller average tumor size (5.1cm vs 6.4cm, p = 0.009). The SP had longer operative time (178 ± 84min vs 142 ± 75min; p = 0.011) but showed no significant difference in the estimated blood loss, blood transfusion rate, length of stay, overall 30-day and major complication rates. Controlling for other variables, SP was significantly associated with a longer operative time and shorter length of stay. SP is associated with longer operative time but offers advantages such as smaller incisions and shorter hospital stays with a comparative safety profile to MP for radical nephrectomy.

Scheme evaluation method of coal gangue sorting robot system with time-varying multi-scenario based on deep learning.

The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

Silicon caregivers: a multilevel analysis of European perspectives on robotic technologies for elderly care

ABSTRACT As populations age, women's workforce participation grows and AI advances, understanding attitudes towards care technologies becomes increasingly important. This study examines attitudes towards robot caregivers for older adults across 28 European countries, using 2017 Eurobarometer data from working-age adults (20–59; n = 13,839). We investigate how individual characteristics (gender, education, occupation) and local-level factors (GDP, female labour participation, over 65s as % of the population, spending on old age) are associated with comfort levels with domestic care robots. The results show that men, individuals with higher levels of educational attainment, and those in professional or managerial occupations report greater comfort with robot caregivers. Additionally, local context plays a significant role. Those living in local communities with higher female employment rates and lower spending on old-age support demonstrate higher acceptance of robotic care solutions. These findings highlight the dual influence of individual and community factors in shaping attitudes towards automated care technologies.

New colleague or gimmick hurdle? A user-centric scoping review of the barriers and facilitators of robots in hospitals.

Healthcare systems are confronted with a multitude of challenges, including the imperative to enhance accessibility, efficiency, cost-effectiveness, and the quality of healthcare delivery. These challenges are exacerbated by current healthcare personnel shortages, prospects of future shortfalls, insufficient recruitment efforts, increasing prevalence of chronic diseases, global viral concerns, and ageing populations. To address this escalating demand for healthcare services, healthcare systems are increasingly adopting robotic technology and artificial intelligence (AI), which promise to optimise costs, improve working conditions, and increase the quality of care. This article focuses on deepening our understanding of the barriers and facilitators associated with integrating robotic technologies in hospital environments. To this end, we conducted a scoping literature review to consolidate emerging themes pertaining to the experiences, viewpoints perspectives, and behaviours of hospital employees as professional users of robots in hospitals. Through screening 501 original research articles from Web-of-Science, we identified and reviewed in full-text 40 pertinent user-centric studies of the integration of robots into hospitals. Our review revealed and analysed 14 themes in-depth, of which we identified seven as barriers and seven as facilitators. Through a structuring of the barriers and facilitators, we reveal a notable misalignment between these barriers and facilitators: Finding that organisational aspects are at the core of most barriers, we suggest that future research should investigate the dynamics between hospital employees as professional users and the procedures and workflows of the hospitals as institutions, as well as the ambivalent role of anthropomorphisation of hospital robots, and emerging issues of privacy and confidentiality raised by increasingly communicative robots. Ultimately, this perspective on the integration of robots in hospitals transcends debates on the capabilities and limits of the robotic technology itself, shedding light on the complexity of integrating new technologies into hospital environments and contributing to an understanding of possible futures in healthcare innovation.

Application of Indoor Mobile Robot Technology in the Construction of 3D Multimedia City Museum Exhibition Hall

Application of Indoor Mobile Robot Technology in the Construction of 3D Multimedia City Museum Exhibition Hall

Robotic Warehousing Operations: A Learn-Then-Optimize Approach to Large-Scale Neighborhood Search

The rapid deployment of robotics technologies requires dedicated optimization algorithms to manage large fleets of autonomous agents. This paper supports robotic parts-to-picker operations in warehousing by optimizing order–workstation assignments, item–pod assignments, and the schedule of order fulfillment at workstations. The model maximizes throughput, managing human workload at the workstations and congestion in the facility. We solve it via large-scale neighborhood search with a novel learn-then-optimize approach to subproblem generation. The algorithm relies on an off-line machine learning procedure to predict objective improvements based on subproblem features and an online optimization model to generate a new subproblem at each iteration. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches. In particular, our solution enhances the utilization of robotic fleets by coordinating robotic tasks for human operators to pick multiple items at once and by coordinating robotic routes to avoid congestion in the facility. Funding: This research was partially funded by Amazon.com Services LLC under award number 2D-12552417. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2024.0033 .

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Technological State and Optimization Analysis of High-Rise Glass Curtain Wall Cleaning Robots

Abstract. With the proliferation of high-rise buildings in urban areas, glass curtain walls are widely used due to their excellent light transmittance and modern appearance. However, these glass curtain walls are prone to accumulating dust, rain stains, and other dirt during use, affecting their aesthetics and transparency, thus requiring regular cleaning. Traditional manual cleaning methods are inefficient and pose significant safety risks. Consequently, high-rise glass curtain wall cleaning robot technology has become a research hotspot. This paper reviews the current research status of high-rise glass curtain wall cleaning robots, which provides a detailed analysis of four main adhesion methods (magnetic adhesion, thrust adhesion, vacuum negative pressure adhesion, and bionic adhesion), four main motion modes (legged, wheeled, tracked, and external assistance), and two main cleaning methods (physical and chemical). For path planning, this paper explores the application status and challenges of regular scanning, random walking, sensor feedback dynamic planning, and bionic path planning. Through the evaluation of the advantages and disadvantages of existing technologies, this paper identifies the main issues faced by each technology and proposes optimization strategies for adhesion technology, motion modes, cleaning efficiency, and path planning. Future directions include improvements in intelligence and automation, multifunctional integration, energy efficiency enhancement, and human-machine collaboration. This paper aims to provide a comprehensive technological review and constructive suggestions to advance high-rise glass curtain wall cleaning robot technology.

A modified extended Kalman filter based fusion of auto-correlation data for robot SLAM estimation

Abstract. With the popularization of robot technology, various technological developments related to robots have gradually entered a stage of rapid development. Especially path planning technology, which has been a very important and worthwhile part of robot motion and behavior since the concept of robots emerged. In this technology, various obstacles, environmental changes, and efficiency factors usually need to be considered. Whether in the past, present, or future, robot behavior pursues greater precision, reliability, adaptability to the environment, and even anthropomorphism. In order to achieve these goals, the idea of data fusion has emerged in the development of robot data processing. Therefore, the research direction is to use Kalman filtering for noise filtering and data correction to improve the accuracy of fused data. By using a feasible and strongly correlated data fusion method, combined with an improved extended Kalman filter, various data generated by robots can be fused in multiple dimensions and have correlations, reducing the negative impact of data differences and improving the quality and reliability of data fusion.

Development of Intelligent Construction Robots for In-situ Lunar Resource Utilization

Abstract: With the development of technologies such as 3D printing and artificial intelligence, robotic lunar construction has gradually become a reality. In recent years, many countries have conducted research on lunar construction, among which lunar in-situ resource utilization construction is the most promising option for lunar construction, and intelligent robot construction is the main method of in-situ resource utilization construction. This paper analyzes the trend of intelligent construction and the necessity of lunar construction, and concludes that lunar construction robots have great development prospects and value. Using "ISRU, intelligent construction robot, 3D printing" as keywords, using the method of literature analysis, literature retrieval is conducted on China National Knowledge Infrastructure and Google Scholar databases, and the types of lunar bases, the acquisition of lunar resources, the classification of intelligent construction robots, and the 3D printing technology and Fully adaptable robot assembly and construction technology in lunar in-situ resource utilization are analyzed. The concept, classification, function, auxiliary 3D printing technology and materials required for 3D printing are analyzed in detail. The assembled mortise and tenon structure, fully adaptable assembly robot technology and underground construction technology are described in detail. The intelligent construction technology involved in lunar in-situ resource utilization construction (ISRU) and how to adapt to the lunar environment for construction are systematically analyzed, and the challenges faced in realizing robotic lunar construction are briefly outlined. It provides ideas for scholars studying intelligent construction robots.

Prediction of the material removal rate in bonnet polishing using a Bayesian optimization deep neural network

The material removal mechanism in robotic bonnet polishing is complex and influenced by multiple factors, necessitating an appropriate method to establish a material removal model. This study employs a Bayesian optimized deep neural network (BO-DNN) to model the intricate relationship between polishing parameters and material removal rate (MRR) using removal function spot experimental data. The tree-structured Parzen estimator (TPE) improves model convergence speed and accuracy, while particle swarm optimization (PSO) assists in inverse verification. Results show that the BO-DNN model achieves a root mean square error (RMSE) of 0.0293 and a Pearson correlation coefficient (PCC) of 99.42% for the total sample, representing approximately a 50% improvement in predictive accuracy over the unoptimized DNN model. The inverse verification results closely match the experimental data, confirming the model’s reliability. This study offers theoretical insights and practical references for advancing robotic bonnet polishing technology.

Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections

Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections

Robotic-Assisted Endodontics: A Future Perspective

Endodontics, a branch of dentistry focused on the diagnosis and treatment of dental pulp and periapical diseases, is undergoing a technological renaissance. Robotic-assisted endodontics is at the forefront of this change, leveraging advanced technologies to improve procedural accuracy, efficiency, and patient comfort. This article explores the current applications of robotic technology in endodontics, discusses the advantages and challenges, and outlines potential future directions for this innovative approach, ultimately aiming to revolutionize dental care.

The Role Of Robotics Technologies In Preschool Mother Tongue Development Of Children With Special Needs

The Role Of Robotics Technologies In Preschool Mother Tongue Development Of Children With Special Needs

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP).

Design, Optimization, and Autonomous Construction of Martian Infrastructure including Slabs Using In-situ CO2-Based Polyethylene

A crucial development in 21st-century space exploration is the establishment of human settlements on Mars. In recent years, Mars exploration rovers have been deployed, and studies investigating human journeys to Mars have been conducted, with the objective of a human-crewed mission to Mars before 2050. This study presents an innovative approach to designing and optimizing Martian habitats by using in-situ CO2-based polyethylene (PE) as a construction material for Martian buildings and greenhouses. It seeks to develop sustainable and resilient habitats on Mars, leveraging local resources to address the environmental challenges specific to the Martian environment. The research targets several key areas: realistic construction techniques, effective utilization of in-situ materials, and building resilience under the harsh Martian conditions. These conditions include radiation, micrometeoroid impacts, low atmospheric pressure, low gravity, wind storms, temperature extremes, Paschen discharge, and marsquakes. Finite element analysis (FEA) of several pressurized spherical structures is used to design and optimize for minimum material usage, inspired by soap bubbles that naturally stick together with separating walls. The proposed habitat design features a central spherical structure surrounded by nine smaller spheres, optimized for material efficiency. It is demonstrated that utilizing two layers of 0.1 m thick PE and a 0.5 m thick CO2 layer in between them offers significant protection against Martian environmental loads such as radiation shielding and thermal insulation. It facilitates light transmission into the building. This study offers a realistic structural system for buildings and greenhouses that can be constructed using in-situ materials and is suitable for an optimal 539-day stay on Mars. The proposed autonomous robotic arm technology can perform all necessary tasks, from producing PE to automatically assembling the Mars Spherical Building (MSB), including in-place slab printing.

Mapping of Artificial Intelligence and Robotics Technologies Applied to Offshore Wind Energy

Mapping of Artificial Intelligence and Robotics Technologies Applied to Offshore Wind Energy