Collaborative System Research Articles

Intelligent multi-robot collaborative transport system

The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

Integration of Artificial Vision and Image Processing into a Pick and Place Collaborative Robotic System

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified.

Exploring the collaborative governance of Olympic stadium renovations: a multiple case study

ABSTRACT Research question Drawing on a collaborative governance framework, this study examined the collaborative governance of Olympic stadium renovation projects in two Olympic host communities: Los Angeles and Atlanta. Research methods Data included archival materials and 13 semi-structured interviews with relevant stakeholders involved in the stadium renovations in LA and Atlanta. Results and Findings This research offers a comprehensive picture of the collaborative governance systems of the post-Games venue renovation projects including multiple elements and components, such as general system contexts, drivers, dynamics, actions, and outcomes. Implications We provide theoretical and practical implications that extend our understanding of post-Games venue projects through effective collaborative governance systems.

Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy.

A novel MR remote collaborative assembly system using reconstructed attribute-enhanced product models

A novel MR remote collaborative assembly system using reconstructed attribute-enhanced product models

A Unified Collision Avoidance Trajectory Planning with Dual Variables for Collaborative Aerial Transportation Systems

As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs.

Design, construction and evaluation of collaborative bio-system of Vibrio fluvialis with Chlorella sorokiniana for treating actual printing and dyeing wastewater

Treating actual printing and dyeing wastewater (APDW) using bacteria/algae collaborative is considered an eco-friendly and cost-effective strategy. In this study, Vibrio fluvialis (V. fluvialis) and Chlorella sorokiniana (C. sorokiniana) were isolated from APDW and used to construct the bacteria/algae collaborative treatment system (BACTS) for treating APDW. The results showed that the V. fluvialis / C. sorokiniana collaborative treatment could achieve removal rates of 87.31 % for TP, 73.23 % for COD, and 35 % for dyes under the optimized conditions (V. fluvialis first added, C. sorokiniana followed, V. fluvialis / C. sorokiniana inoculation ratio of 1:2, pH 6.5 and light intensity 8,000 Lux). The APDW water quality improved 55.6 % after the V. fluvialis / C. sorokiniana collaborative treatment. Additionally, the collaborative system generated a significant amount of high-value products (27.63 % lipid yield, 81.1 mg/g polysaccharide and 16.62 mg/g protein). The transcriptome analysis demonstrated that the expression of various enzymes such as laccases, peroxidases and azoreductases, etc. related pathways in V. fluvialis / C. sorokiniana involved in textile dyes degradation and biotransformation. This study provides an efficient and low-cost treatment method for APDW through a novel synergistic biological system of bacteria and algae.

Robust Collaborative Visual-Inertial SLAM for Mobile Augmented Reality.

Achieving precise real-time localization and ensuring robustness are critical challenges in multi-user mobile AR applications. Leveraging collaborative information to augment tracking accuracy on lightweight devices and fortify overall system robustness emerges as a crucial necessity. In this paper, we propose a robust centralized collaborative rnulti-agent VI-SLAM system for mobile AR interaction and server-side efficient consistent mapping. The system deploys a lightweight VIO frontend on mobile devices for real-time tracking, and a backend running on a remote server to update multiple submaps. When overlapping areas between submaps across agents are detected, the system performs submap fusion to establish a globally consistent map. Additionally, we propose a map registration and fusion strategy based on covisibility areas for online registration and fusion in multi-agent scenarios. To improve the tracking accuracy of the frontend on agent, we introduce a strategy for updating the global map to the local map at a moderate frequency between the camera-rate pose estimation of the frontend VIO and the low-frequency global map optimization, using a tightly coupled strategy to achieve consistency of the multi-agent frontend poses estimation in the global map. The effectiveness of the proposed method is further confirmed by executing backend mapping on the server and deploying VIO frontends on multiple mobile devices for AR demostration. Additionally, we discuss the scalability of the proposed system by analyzing network traffic, synchronization frequency, and other factors at both the agent and server ends.

OPTIMALISASI POTENSI PENDAPATAN MELALUI INOVASI SI-ERPIN (SISTEM INFORMASI ENTERPRISE RESOURCE PLANNING INNOVATION)

PT Dunia Sandang Pratama is a fabric supplier company experiencing difficulties monitoring orders, ensuring timely deliveries, and producing accurate financial reports. These challenges have an adverse impact on customer satisfaction and operational efficiency. Because of the acute urgency of these issues, this community service project is designed to develop an integrated ERP system with e-commerce functionality to overcome the existing problems, increase efficiency, expand market reach and provide more accurate information to management. This community service is aligned with the United Nations Sustainable Development Goals (SDGs), particularly in fostering innovation and narrowing the digital divide. The service team, comprising four lecturers and five students, employed the Scrum methodology, which is flexible and interactive, to observe, analyse and design the system in collaboration with the partner, PT Dunia Sandang Pratama. The project's initial phase has been completed, including developing e-commerce features and sales modules. The preliminary results indicate an increase in efficiency concerning order management and reporting. Implementing the ERP system has enhanced data accuracy, operational efficiency, and employee competence. From a quantitative perspective, order processing time has been reduced, the incidence of data errors has decreased, and company revenue has increased. From a qualitative standpoint, employee competence in ERP usage has also improved, positively impacting decision-making. In conclusion, the project has successfully supported the company's decision-making processes.

Generic Diagramming Platform (GDP): a comprehensive database of high-quality biomedical graphics.

High-quality schematic illustrations are fundamental to the publication of scientific achievements in biomedical research, which are crucial for effectively conveying complex biomedical concepts. However, creating such illustrations remains challenging for many researchers due to the need to devote a significant amount of time and effort to accomplish it. To address this need, we present the Generic Diagramming Platform (GDP, https://BioGDP.com), a comprehensive database of professionally crafted biomedical graphics (bio-graphics). Currently, GDP houses 7 562 high-quality bio-graphics, meticulously categorized into 10 major and 77 minor categories. To increase the design efficiency, GDP provides 204 customizable templates derived from an extensive review of over 2000 literature and 7 textbooks. With the interactive drawing platform and user-friendly web interface implemented in GDP, these resources can facilitate the efficient generation of publication-ready illustrations for the biomedical community. Additionally, GDP incorporates a collaborative submission system, allowing researchers to contribute their artwork, fostering a growing diagramming ecosystem, and ensuring continuous database expansion. Overall, we believe that GDP will serve as an invaluable platform, significantly enhancing the efficiency and quality of scientific illustration for biomedical researchers.

Resilient Sustainability Assessment Framework from a Transdisciplinary System-of-Systems Perspective

The vital role of extensive information exchange among stakeholders across diverse sectors and the interconnection of various scientific fields with nonhomogeneous technology readiness levels has created a new form of a complex engineering problem in the climate change era. Comprehensive sustainability assessment to enable the realization of needs requires transdisciplinary thinking to achieve systematic solutions that bridge the gap between multiple collaborative systems in a portfolio. Although the principal aim of dedicated sustainability regulations is to force companies to move toward sustainability development, general and non-engineered metrics that have not defined clear thresholds for evaluation have encountered severe challenges regarding implementation and economic viability. Therefore, adopting a transdisciplinary systems engineering approach can address multifaceted challenges like sustainability by overcoming collaboration barriers, and traditional disciplinary limits. This paper systematically reviews sustainability-dictated regulations from a transdisciplinary perspective. Different standards are compared, raised opportunities and challenges are discussed, and future remarks are highlighted. The sustainability problem is analyzed from a transdisciplinary systems engineering lens. Finally, a two-level resilient system sustainability assessment framework is proposed to effectively handle and enhance the resilience of companies’ sustainability development roadmaps by enabling decision makers to find robust and highly reliable solutions regarding sustainable system design. The impact of this research is to create a new insight into addressing climate change which not only assesses the current situation but also considers uncertainty sources that affect decision making for the future.

From Conflict to Cooperation: Norwegian Labour Market Institutions in the Making

Moving a relationship of widespread conflicts and distrust into a virtuous circle of trust and cooperation is challenging, yet the case examined in this article shows that it is possible. A problematic start can indeed lead to a positive outcome. In the early twentieth century, Norway experienced the highest levels of labour conflict in Europe. Class conflicts were intense and often violent, with deep mistrust between the parties in the labour market. This situation changed markedly when the Norwegian Employers’ Confederation and the Norwegian Confederation of Trade Unions signed the Basic Agreement in 1935. This agreement not only led to a lasting decline in labour conflicts, but it also marked a crucial early step towards a system of close collaboration between labour and capital in Norway. By focusing on the intimate relationship between beliefs, trust, and cooperation, this article explores how the labour market parties transitioned from widespread conflict and open struggle to a system of peaceful negotiations and compromises. Changes in beliefs enabled the parties to coordinate on a new cooperative equilibrium, an equilibrium that continues to characterise Norway’s labour market today.

Effects of Collaborative Support on Learning in Serious Games for Programming

This study examines the effectiveness of a collaborative support method in enhancing programming learning outcomes in a serious game. The game Code Hasp was developed to facilitate the comparison between two support methods, worked examples and a collaborative peer support approach. A total of 111 elementary school students participated, divided into two groups. The experimental group received collaborative support from teammates alongside worked examples, while the control group relied solely on worked examples. Pre- and post-test results and in-game data revealed that the collaborative support method improved learning efficiency and problem-solving skills. A notable effect of the process was the emergence of a leadership role in most collaborative groups. The game’s support design and shared goal structure mitigate potential problems associated with unbalanced participation. These findings suggest that collaborative support systems in serious games are a promising strategy for promoting engagement and improving learning outcomes in programming education.

1.N. Scientific session: Child injuries monitoring and reporting: Challenges and technological developments

Abstract Globally, more than 1,600 children and adolescents below the age of 19-years die every day from preventable injuries. Road traffic injuries, drowning and self-harm represent leading causes of death and injury among children and adolescents (WHO 2021; WHO Regional Office for Europe, 2021; Unicef, 2021). Most unintentional injuries in early childhood occur at home and children from less advantaged backgrounds are exposed to more hazardous home environments due to overcrowding or unsafe structures over which parents often have limited control (WHO, 2020). According to the IDB data analysis approximately 15% of home injuries could have been prevented by improved product safety and parental education. The COVID-19 pandemic and the conflict in Ukraine have created a complex and challenging situation for children and adolescents in the region, increasing their vulnerability and compromising their well-being. To efficiently prevent child injuries, one must account for a complex interplay between human factors as well as physical and socio-cultural environments. Multiple determinants of child injury cannot be addressed by the health sector alone, this is why a whole-of-government and a whole-of-society approaches are required. Schools have a crucial role to play for promoting the well-being of children and serve as a platform to increase access to health services. However, EU countries are still missing intersectoral collaboration and strong information systems to enable efficient response to the problem. Data on child injuries are still inconsistently collected in Europe due to substantial differences in coding systems, variation between injury mechanisms, different general policies pertaining to the admission of patients, and legal policies on reporting requirements. EU-wide networks such as the Eurosafe study (IDB network) collecting information on child injuries, are scarce. Likewise, access to healthcare services and support systems are unevenly distributed, particularly in underserved communities. Building country capacity in collecting, analysing and increasing the use of child injury data for decision making at global, regional, and national levels, is critical. Adequate epidemiological data on the burden of child injuries are necessary to allow governments prioritize areas for intervention and maximize the impact of their resources. Utilizing evidence-based tools and procedures and promoting intersectoral collaboration is vital. The workshop aims to discuss trends and inequalities of injury burden and present efficient measures and latest developments in monitoring and reporting child injuries across EU countries. The workshop will further discuss the methodological challenges in collecting and analysing data on child injuries. Evidence-informed tools for sharing health information and improved measurements of injury burden that are feasible and usable in both research and clinical contexts will be presented and success stories will be presented. Key messages • There is an EU-wide need for epidemiological data on the burden of child injuries. • Validated tools, well-designed injury data management systems and standard data collection procedures are missing.

Recommendations for Better Collaborative Groundwater Monitoring for the US High Plains Aquifer

Groundwater is a critical resource for the Great Plains region of the United States, providing drinking water for over 2 million people. However, the High Plains Aquifer (HPA) is under significant threat from over-extraction—defined as the excessive withdrawal of groundwater beyond its natural replenishment rate. This overuse jeopardizes not only water availability but also equitable access and allocation. Effective groundwater monitoring is essential to track trends in water availability, assess the impacts of extraction, and develop strategies to ensure long-term sustainability. Without comprehensive monitoring, it is difficult to address key issues like contamination, depletion, and groundwater quality degradation. While several state-level frameworks exist to enhance groundwater monitoring, they operate independently, leading to gaps in data sharing and collaboration, especially for a transboundary resource like the HPA. The Environmental Protection Agency (EPA) and U.S. Geological Survey (USGS) are well-positioned to play a more central role in this process. The EPA, with its mandate to protect water quality, and the USGS, with its expertise in nationwide data collection, are critical to supporting a collaborative and comprehensive groundwater monitoring system. By facilitating the integration of state-level efforts, these federal agencies can help ensure that groundwater monitoring is both consistent and accurate, enabling effective decision-making at regional and national levels. This policy memo provides a detailed analysis of current state-level efforts, highlights the role of the EPA and USGS in addressing governance challenges, and proposes a transboundary governance mechanism to enhance collaborative groundwater monitoring. The intended audience includes policymakers at the EPA and USGS, as well as water resource managers who are key to implementing these changes.

A collaborative system for recommending a service within the cloud using deep learning

A collaborative system for recommending a service within the cloud using deep learning

An Automated Word Spelling Error Detection System

Accurate spelling has extensive consequences for written communication's clarity, credibility, and general effectiveness in several contexts, including academic and professional writing and personal communications. The frequency of spelling errors has increased significantly recently, mainly owing to the widespread use of digital media and society's dependence on written communication. Misspelled words can diminish the reliability of written correspondence and result in misinterpretations. Hence, there is an urgent requirement for an innovative and advanced solution to improve the identification of spelling errors. This research seeks to address these challenges and develop a system that significantly enhances the quality of written communication by improving the accuracy and relevance of spelling error detection and correction suggestions. According to this study, the implemented model is a promising technique to correct spelling problems in legal terminology, has been praised for its effectiveness and usability. Its recommendations include continuous research and development, improving its ability to use contextual cues and domain-specific information for error correction, emphasizing user-centric customization, implementing collaborative feedback systems, offering extensive training and support, prioritizing ethical and regulatory considerations, and fostering interdisciplinary collaboration. These suggestions aim to optimize the Spello model's performance, resilience, and scalability, and make it a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society. The model can become a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society through combined efforts and collaboration.

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning

This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

How do collaborative systems affect organizational agility and performance in supply chains?

How do collaborative systems affect organizational agility and performance in supply chains?