Real-time Interaction Research Articles

DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network

In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impacts the entire system’s reliability by reducing its response speed and real-time decision-making. Consequently, there is an imperative demand for a lightweight approach to tackle the challenges arising from the escalating scale of digital twin virtual entity models. This paper presents a digital twin methodology that is lightweight. The procedure comprises three primary phases: graph data modeling, graph neural network analysis, and hierarchical simplification of virtual entities. Specifically, the graph neural network method proposed in this article is used to classify the graph data of virtual entities. Then, the model is hierarchically simplified based on the classification. Finally, experiments were conducted on factory and robotic arm datasets to evaluate the proposed method. The experimental results indicate that the DTL-GNN method can reduce system redundancy while preserving the essential features of virtual entities.

A parameter-interactive digital twin model of bearings under variable speeds

The digital twin model plays a crucial role in the Prognostics and Health Management of mechanical systems. However, current digital twin models for bearings lack comprehensive research on the interaction between the virtual entity and the physical entity under variable speeds. Accordingly, a parameter-interactive digital twin model has been proposed to interact the information of bearings between virtual entity and physical entity in real-time under variable speeds. Firstly, a bearing dynamics model considering speed, stiffness and fault size was established under variable speeds. Secondly, speed parameter was updated in accordance with the ridgeline of the power spectrum. Stiffness and fault size parameters were updated through an iterative extended Kalman filtering process. Condition of the bearing was monitored through various parameters. The accuracy of the dynamic model and the feasibility of parameter interaction were validated through experimental studies. These findings demonstrate that the proposed method can effectively ensure real-time parameter interaction and condition monitoring under variable speeds within the digital twin model.

Abstract TP361: Type 2 Diabetes Enhances Leukocytes And Brain Endothelial Cells Adhesion Interactions Following Ischemic Stroke

Introduction: Stroke is a leading cause of death and disability worldwide, with diabetes being a significant risk factor that exacerbates stroke outcomes. While previous studies have identified various cell populations characteristic of stroke and other neurological diseases through transcriptomic analysis of brain and blood cells, the interaction between leukocytes and brain endothelial cells during stroke remains inadequately understood. This study aimed to investigate these intercellular interactions in the context of diabetes and stroke. Methods: We utilized a permanent ischemic stroke model in leptin receptor-deficient db/db mice (a model for type 2 diabetes) and their non-diabetic counterparts, db/+ mice. Three days post-stroke, brain cells and circulating leukocytes were isolated and subjected to single-cell RNA sequencing (scRNAseq). The CellChat tool was employed to analyze the intercellular communication between these cells. Additionally, in vivo two-photon microscopy was used to observe real-time interactions between leukocytes and brain endothelial cells in cortical veins. Results: scRNAseq analysis revealed that diabetic mice had a higher proportion of myeloid cells in the blood compared to controls. Post-stroke, both diabetic and non-diabetic mice showed an increased presence of microglia and macrophages in the brain. CellChat analysis indicated that both diabetes and stroke significantly elevated the number and strength of cell-cell interactions, with the highest levels observed in the diabetic stroke group. Notably, interactions involving the integrin family and ICAM/VCAM, known for their roles in cell adhesion between blood leukocytes and brain endothelial cells, were particularly prominent in the diabetic mice. In vivo imaging confirmed significant leukocyte accumulation in the cortical veins immediately after stroke in both genotypes, with delayed clearance observed in the diabetic group. Conclusion: The findings suggest that diabetes exacerbates the inflammatory response in stroke by enhancing leukocyte-endothelial interactions, leading to prolonged leukocyte retention in the cerebral vasculature, which likely contributes to increased inflammation and worsened stroke outcomes in diabetic conditions.

Personalized Cognitive Support via Social Robots

This paper explores the use of personalized cognitive support through social robots to assist the elderly in maintaining cognitive health and emotional well-being. As aging populations grow, the demand for innovative solutions to address issues like loneliness, cognitive decline, and physical limitations increases. The studied social robots utilize machine learning and advanced sensor technology to deliver real-time adaptive interactions, including cognitive exercises, daily task assistance, and emotional support. Through responsive and personalized features, the robot enhances user autonomy and improves quality of life by monitoring physical and emotional states and adapting to the needs of each user. This study also examines the challenges of implementing assistive robots in home and healthcare settings, offering insights into the evolving role of AI-powered social robots in eldercare.

PEBP1 amplifies mitochondrial dysfunction-induced integrated stress response.

Mitochondrial dysfunction is involved in numerous diseases and the aging process. The integrated stress response (ISR) serves as a critical adaptation mechanism to a variety of stresses, including those originating from mitochondria. By utilizing mass spectrometry-based cellular thermal shift assay (MS-CETSA), we uncovered that phosphatidylethanolamine-binding protein 1 (PEBP1), also known as Raf kinase inhibitory protein (RKIP), is thermally stabilized by stresses which induce mitochondrial ISR. Depletion of PEBP1 impaired mitochondrial ISR activation by reducing eukaryotic translation initiation factor 2α (eIF2α) phosphorylation and subsequent ISR gene expression, which was independent of PEBP1's role in inhibiting the RAF/MEK/ERK pathway. Consistently, overexpression of PEBP1 potentiated ISR activation by heme-regulated inhibitor (HRI) kinase, the principal eIF2α kinase in the mitochondrial ISR pathway. Real-time interaction analysis using luminescence complementation in live cells revealed an interaction between PEBP1 and eIF2α, which was disrupted by eIF2α S51 phosphorylation. These findings suggest a role for PEBP1 in amplifying mitochondrial stress signals, thereby facilitating an effective cellular response to mitochondrial dysfunction. Therefore, PEBP1 may be a potential therapeutic target for diseases associated with mitochondrial dysfunction.

Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city.

What drives trust building in live streaming E-commerce? From an elaboration likelihood model perspective

PurposeLive streaming e-commerce, which integrates real-time video interaction with online shopping, has quickly become a popular sales channel. It not only allows for immediate feedback but also builds a sense of trust and connection between streamers and consumers. Drawing on the elaboration likelihood model (ELM), we investigate how central and peripheral route factors affect consumers’ trust building and purchase intentions. Additionally, we identify consumer involvement as a key moderator affecting the relationship between central route factors and trust in product as well as the relationship between peripheral route factors and trust in streamer.Design/methodology/approachTo test the research model, we collected data from 423 consumers on TaoBao Live.FindingsThe findings show that information completeness, accuracy and currency positively affect trust in the product, while perceived physical characteristic similarity, streamer humor attractiveness and passion attractiveness positively affect trust in the streamer. Trust in the streamer positively influences trust in the product, which subsequently impacts purchase intention. Moreover, involvement moderates the effects of information accuracy, currency, perceived physical characteristic similarity and passion attractiveness on trust.Originality/valueFirst, we examine the direct influence of product- and streamer-related cues on consumer trust and purchase intention through distinct pathways. Second, we adopt ELM to explain the process of consumer trust building by investigating how central and peripheral route factors influence purchase intention through consumer trust in live streaming settings. Third, we incorporate involvement as a crucial moderator, shedding light on the boundary conditions of trust building in live streaming e-commerce.

Mobile Application with Artificial Intelligence Chatbots for Dengue Information and Management

Dengue fever has intensified in recent years, affecting millions, with an estimated 100 to 400 million people yearly; approximately 80% of these cases have been reported in the Americas. This develops a prototype of a mobile application that includes artificial intelligence (AI) chatbots for dengue management and information. The design thinking methodology was applied, which was useful due to its focus on user needs, allowing the identification of key problems in dengue management and the design of innovative and functional solutions. The results were validated by experts using criteria such as technology, innovation, and feasibility with an average score of 4.26 and users using criteria such as functionality, usability, and accessibility with an average score of 4.28, showing a high level of acceptance. This tool has the potential to improve early disease detection, facilitating data collection and real-time interaction with patients. Through the use of AI, the application aims to optimize case management, reduce serious complications, and support health systems in at-risk areas.

Leveraging Mobile Interaction Technologies for Real-Time Decision Making in Enterprise Management Systems

With the widespread adoption of mobile internet and smart devices, enterprises face rapidly changing market environments and complex decision-making requirements. How to utilize mobile interaction technologies to enhance real-time decision-making capabilities in enterprise management systems has become a critical issue in modern business management. Through real-time data transmission and interaction via mobile devices, businesses can more flexibly acquire information, monitor statuses, and optimize decision-making processes, thereby improving the timeliness and accuracy of decisions. Existing research primarily focuses on traditional decision support systems (DSS) and data mining technologies, but these methods fall short when addressing the dynamic and real-time decision-making needs, especially in complex enterprise management scenarios. One major challenge remains the effective integration of multiple data sources. Therefore, leveraging real-time interactive data from mobile technologies to improve decision support in enterprise management systems becomes a pressing research topic. This paper proposes a real-time decision-making framework for enterprise management systems based on mobile interaction technologies, focusing on the problem description of real-time decision-making in enterprise management and a decision-making mining framework based on mobile interaction relationships and mixed user discourse (including decision belief propagation based on mobile interaction relationships and the UCBert model based on user content analysis). Through this framework, the paper aims to enhance decision-making accuracy and timeliness, enabling enterprises to make more informed and efficient decisions in a dynamically changing market.

Effective online methods and pedagogical insights in digital creativity for literature education

The transition to distance learning during the pandemic has highlighted the imperative to adapt all academic disciplines, including literature, to an online format. The aim of this study is to ascertain the most efficacious pedagogical approaches for the remote teaching of literature courses, with a particular focus on the distinctive requirements of artistic interpretation, which has traditionally benefited from real-time interaction. The methodology comprised surveys and experimental analysis conducted with 300 students at the Kazakh National University. The research compared traditional online teaching methods with innovative techniques, including game-based learning, problem-based learning, case studies, brainstorming, multimedia technologies, information literacy skills, and programmed learning. The results demonstrate that these contemporary strategies markedly enhance students’ comprehension and engagement, as evidenced by elevated academic performance and a more profound grasp of the course material. The integration of advanced technologies in distance education provides students with the opportunity to experiment with new ways of expressing their literary understanding. The findings of this study indicate that distance learning in literature can be highly effective when tailored pedagogical strategies and technological tools are employed. However, the management of these systems requires economic decision-making and further social and technological adaptations, particularly for disciplines like literature where a nuanced approach to artistic content is essential. This study provides recommendations for refining online literature courses to optimise student engagement and comprehension in virtual learning environments.

Development and Experimental Validation of a Sense-and-Avoid System for a Mini-UAV

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces

Mixed reality for preoperative planning and intraoperative assistance of surgical correction of complex congenital heart defects.

Mixed reality (MixR) is an innovative visualization tool that presents virtual elements in a real-world environment, enabling real-time interaction between the user and the combined digital/physical reality. We aimed to explore the feasibility of MixR in enhancing preoperative planning and intraoperative guidance for the correction of various complex congenital heart defects (CHDs). Patients underwent cardiac computed tomography or cardiac magnetic resonance and segmentation of digital imaging and communications in medicine (DICOM) images was performed. Three-dimensional models were then uploaded into a MixR headset (Microsoft HoloLens 2), displayed as holograms, and used for preoperative navigation of cardiac anatomy and intraoperative assistance of surgical steps. We adopted MixR in 5 different clinical scenarios: minimally-invasive partial anomalous pulmonary venous connection repair of 5 anomalous veins; intra-extracardiac Fontan completion in right atrial isomerism and dextrocardia; arterial switch operation for transposition of the great arteries with abnormal coronary pattern in situs inversus; complete unifocalization of major aortopulmonary collateral arteries; total artificial heart implantation for failing Fontan. Preoperative MixR allowed a comprehensive understanding of complex anatomical relationships and spatial mapping, using immersive 3D navigation. As a result, the surgeon was able to simulate the surgical approach and plan the best surgical strategy. Intraoperatively, MixR provided a real-time virtual map by overlaying holograms onto the patient's anatomy. MixR represents a promising tool for preoperative planning and 3D visualization in complex CHDs, however, its systematic adoption in the intraoperative setting requires further implementation of current hardware technology and software versatility.

Comparing the efficacy of live vs. video instructional demonstrations in dental education: a systematic review and meta-analysis

BackgroundThis systematic review and meta-analysis were conducted to investigate the effects of live and video demonstrations of various dental procedures on undergraduate students’ performance and satisfaction scores.Materials and methodsA comprehensive search was conducted across multiple databases, including MEDLINE (OVID), PubMed, Scopus, and Web of Science, supplemented by a manual search of bibliographic references from retrieved articles. The aim was to identify relevant randomized controlled trials and quasi-experimental trials that compared live demonstrations to video demonstrations in dental education, specifically focusing on undergraduate students’ performance and satisfaction scores assessed using practical assessment rubrics and satisfaction questionnaires. The quality of included studies was assessed using the modified Downs and Black quality assessment tool.ResultsA total of 3686 studies were identified, of which 2222 studies remained following duplicate removal. Based on title and abstract screening 2188 studies were excluded and full text of 34 studies were comprehensively appraised for eligibility. This resulted in the inclusion of 10 studies in the systematic review, nine of which were classified as good, while one study was considered as fair. Of these, 8 studies were included in the meta-analysis.Students’ feedback reveals distinct preferences, with video demonstrations being commended for repeatability and clarity, while live demonstrations were valued for real-time interaction and guidance. Meta-analysis revealed that video-based learning significantly enhanced educational outcomes across various measures, including knowledge acquisition and practical skills over live demonstrations.ConclusionVideo demonstrations emerge as viable alternatives to live demonstrations in dental education for teaching clinical procedures. Video demonstrations offer distinct benefits, including accessibility and repeatability, while live demonstrations provide essential interactive opportunities that contribute significantly to the learning experience in dental education.

Dynamic Genomic Imaging and Tracking in Living Cells by a DNA Origami-Based CRISPR‒dCas9 System.

The clustered regularly interspaced short palindromic repeat (CRISPR)-associated system has displayed promise in visualizing the dynamics of target loci in living cells, which is important for studying genome regulation. However, developing a cell-friendly and rapid transfection method for achieving dynamic and long-term genomic imaging in living cells with high specificity and accuracy is still challenging. Herein, a robust and versatile method is presented that employs a barrel-shaped DNA nanostructure (TUBE) modified with aptamers for loading, protecting, and delivering CRISPR-Cas9 to visualize specific genomic loci in living cells. This approach enables dynamic tracking of target genomic regions (Chr3q29, a repetitive region of chromosome 3) throughout the mitotic process and captures variations in their spatial distribution and quantity accurately. Distinct dynamic behaviors between the Chr3q29 and telomeres are observed, which are linked to their unique chromosomal positions and levels of mobility. High-resolution multicolor labeling of the target genes is achieved, with a high degree of colocalization between the enhanced green fluorescent protein and cyanine-5 channels, facilitating precise imaging of target loci. This method not only supports dynamic genomic imaging but also enables multiplexed tracking, providing a powerful visualization tool for studying cellular processes and genetic interactions in real time within living cells.

Assistive Communication Web App: A Multi-Model Solution for Individuals with Disabilities”

The Assistive Communication Web App is an innovative platform designed to address the communication challenges faced by individuals with speech, hearing, and motor impairments. By integrating advanced technologies such as speech recognition, head-gaze tracking, and gesture-based sign language recognition, the app provides real-time speech-to-text, text-to-speech, and gesture-to-text functionalities. This research outlines the app's system architecture, methodology, and user interface, emphasizing its potential to enhance accessibility and empower users with greater independence. Through its multi-modal approach, the web app fosters inclusivity and sets a new benchmark for assistive technologies. Keywords: Assistive communication, Speech-to-text, Text-to-speech, Sign language recognition, Head gaze tracking, WebGazer.js, Real-time interaction, Gesture recognition, MediaPipe FaceMesh, TensorFlow HandPose, Web Speech API, Real-time transcription, Multilingual support, Cross-platform compatibility, Emotion detection, User-centered design, Modular architecture

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling.

Functional Near-Infrared Spectroscopy (fNIRS) hyperscanning is an innovative technique that enables real-time monitoring of brain activity among multiple individuals engaged in social interactions. Researchers in this field quantify concurrent brain activities through the index of inter-brain synchrony (IBS). In psychological counseling research, the use of fNIRS to measure IBS has garnered attention for its potential to illuminate the dynamics of counselor-client interactions. Nevertheless, the field currently lacks a standardized protocol for precisely measuring IBS between counselors and clients, which would facilitate the revelation of real-time interaction patterns during counseling sessions. To address this need, this paper proposes a detailed standardized protocol, outlining the procedural steps for conducting fNIRS hyperscanning in psychological counseling settings, focusing on the acquisition of brain signals, calculation of IBS between counselors and clients, and analysis of lead-lag patterns of IBS throughout counseling sessions. Implementing this standardized fNIRS hyperscanning pipeline not only enhances the reproducibility and reliability of IBS measurements in psychological counseling research but also facilitates deeper insights into the neural mechanisms underlying working alliance. By integrating fNIRS hyperscanning into naturalistic counseling environments, researchers can advance understanding of how IBS correlates with counseling outcomes, potentially informing personalized approaches to mental health treatment.

Unraveling Cholesterol-Dependent Interactions of Alkylphospholipids with Supported Lipid Bilayers.

Alkylphospholipids are single-chain lipid amphiphiles that possess clinically relevant biological activities driven by membrane-destabilizing interactions. Subtle variations in alkylphospholipid structure can lead to significant differences in their biological effects, yet corresponding membrane interactions remain underexplored. Herein, we employed the quartz crystal microbalance-dissipation (QCM-D) technique to characterize the real-time membrane interactions of three alkylphospholipids-edelfosine, miltefosine, and perifosine-on supported lipid bilayers with varying cholesterol fractions. Our findings reveal that the tested alkylphospholipids had distinct membrane-interaction profiles: (1) edelfosine exhibited irreversible binding and caused weak membrane disruption; (2) miltefosine caused major disruption by affecting membrane packing; and (3) perifosine exhibited reversible binding while triggering structural rearrangements and modest disruption. Overall, alkylphospholipid micelles showed greater activity than monomers, and higher membrane cholesterol fractions resulted in more extensive disruption, highlighting the interplay between membrane stiffness and responsiveness. These results provide biophysical evidence that different alkylphospholipids have distinct membrane-interaction behaviors that align well with reported biological activities. Our supported lipid bilayer approach offers a valuable platform for designing and assessing alkylphospholipids with tailored membrane-interaction profiles.

Stereotypes and social decisions: The interpersonal consequences of socioeconomic status.

Perceptions of socioeconomic status (SES) can perpetuate inequality by influencing interpersonal interactions in ways that disadvantage people with low SES. Indeed, lab studies have provided evidence that people can detect others' SES and that they may use this information to apply stereotypes that influence interpersonal decisions. Here, we examine how SES and SES-based stereotypes affect real-world social interactions between people from a socioeconomically diverse population. We used the computer-mediated online round-robin method to facilitate interactions among 297 participants from across the U.S. Participants completed a series of dyadic interactions with other participants in virtual rooms in which they discussed a recent negative consumer experience. After each interaction, they judged the interaction partner's SES, personality traits, and credibility of their consumer experience. Results showed that people perceived SES with moderate accuracy in the interactions, which elicited negative interpersonal stereotypes of low-SES individuals for all 12 of the personality traits measured. People also preferred to affiliate with others with high SES, had more sympathy for them, and found their experiences more credible. SES-based interpersonal stereotypes about personality traits mediated these associations. The perception of SES in real-time interactions thus appears to activate stereotypes that guide social judgments, supporting the hypothesis that interpersonal effects contribute to economic inequality. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Gender differences in oxyhemoglobin (oxy-Hb) changes during drawing interactions in romantic couples: an fNIRS study.

Interpersonal interaction is essential to romantic couples. Understanding how gender impacts an individual's brain activities during intimate interaction is crucial. The present study examined gender differences in oxyhemoglobin (oxy-Hb) changes during real-time drawing interactions between members of romantic couples using non-invasive functional near-infrared spectroscopy (fNIRS). We analyzed the oxy-Hb concentrations of romantic couples engaged in interactive (i.e., chase and escape) and non-interactive (i.e., individual) drawing sessions. Our findings indicated that males (vs. females) exhibited more pronounced oxy-Hb concentrations in Broca's area, motor area, sensorimotor cortex, and temporal lobe areas than women in an interactive drawing task, suggesting a heightened goal-oriented engagement in social interaction. Significant positive correlations were found between oxy-Hb volumes of the temporal area and the Quality of Relationship Index (QRI), underscoring the impact of interpersonal dynamics on brain function during interactive tasks. This study deepens the understanding of gender differences in neural mechanisms in social interaction tasks and provides important insights for intimacy research.

Collision avoidance and routing based on location access (CARLA) of mobile robots

Abstract The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.