Autonomous Service Research Articles

Research on Visual Recognition of Service Robots Based on YOLO Algorithm

In modern society, based on the aging of population and the rapid development of intelligent technology, the demand for intelligent service robots is increasing, and visual recognition is one of the key technologies of service robots. The rapid development of deep learning technology provides new solutions for visual recognition. Existing target recognition algorithms have the problem of complex model and slow inference speed. According to the above problems, this paper proposes a visual recognition algorithm based on YOLO series, which is used for the visual recognition design scheme of service robot. It can ensure the detection accuracy, reduce the speed of the model complexity, speed up the reasoning, and determine whether to meet the actual needs. Through randomly selected pictures, the results show that the design scheme can effectively improve the detection accuracy and real-time of the service robot on targets, and enhance its autonomous service ability in various complex environments.

Application of Time Series in the Analysis of Physical-Chemical Data to Support the Management of Water Clarification Efficiency in a Water Treatment Plant

Abstract. Water is a natural and essential resource for life and must be preserved. Its potability is also vital for human survival. In this context, the turbidity parameter is a factor preponderant, as its presence can “protect” harmful microorganisms during the disinfection. Considering the extensive volume of data collected by Pelotas Autonomous Service of Sanitation at the Santa Bárbara Water Treatment Plant over the years, it was proposed to develop a methodology to support decision-making by managers, through data processing using statistical methods, machine learning and time series. Studies have shown that turbidity is an essential parameter in quality monitoring of the water andcan be inferred before the filtration stage from data collected throughout time. Furthermore, using data from the treatment plant itself offers the advantage of taking its peculiarities into account in the analysis.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation.

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Multi-task federated learning-based system anomaly detection and multi-classification for microservices architecture

The microservices architecture is extensively utilized in cloud-based application development, characterized by the construction of applications through a series of functionally independent, small, autonomous services. This architectural approach is renowned for its attributes such as high cohesion, availability, low coupling, and exceptional scalability. The detection of runtime system point anomalies in microservices architectures is crucial for enhancing the Quality of Service(QoS). Furthermore, identifying the classes of detected anomalies is critical in practical applications. However, given the highly dynamic nature of microservices systems as a distributed computing architecture, conducting real-time system anomaly detection on distributed independent microservices poses a challenging task. To address these challenges, we propose the System Anomaly Detection and Multi-Classification based on Multi-Task Feature Fusion Federated Learning (SADMC-MT-FF-FL) framework. Initially, we introduce a distributed learning framework based on Multi-task Federated Learning (MT-FL) to construct multi-classification anomaly detection models for each microservice. Secondly, to identify complex system anomaly patterns and features during the runtime of microservices, we develop a feature extractor based on External Attention Mechanism and Multi-channel Residual Structure (EA-MRS). Finally, we design a Local–Global Feature-based Parallel Knowledge Transfer (LGF-PKT) framework, utilizing parallel knowledge transfer to parallelize weight updates for local and global features. To validate the effectiveness of our approach, we conducted comprehensive comparative experiments on the microservices benchmark platforms Sock-Shop and Train-Ticket. The experimental results on anomaly detection for multiclassification systems demonstrate that SADMC-MT-FF-FL outperforms the best baseline method by 28.3% and 27.8% for Macro F1 and Micro F1 on Train-Ticket, and by 8.8% and 8.6% on Sock-Shop, respectively. Additionally, we conducted comparison experiments on three public datasets, SWaT, SMD, and SKAB. The F1 scores were 0.5% higher than those of the centralized methods on SMD, respectively, 6% and 2.8% higher than those of the federated learning based method on SWaT and SKAB. Source codes are available at: https://github.com/icc-lab-xhu1/SADMC-MT-FF-FL.

Autonomic IoT Application Placement in Edge/Fog Computing

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of microservices that are mapped to edge computing and communication devices (fog nodes). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

МЕТОД РЕКОНСТРУКЦІЇ ДОДАТКІВ ДЛЯ СТВОРЕННЯ КОНТЕЙНЕРІВ РОЗПОДІЛЕНОЇ МІКРОСЕРВІСНОЇ АРХІТЕКТУРИ

Abstract. Microservices are small, autonomous services. An application consisting of such microservices can be developed separately, with each microservice processing its own part of the logic without interacting with the others. Microservices are easy to deploy in containers, and container orchestration technologies help manage their distribution and updates. Therefore, many companies are moving from monolithic architectures to microservices. This article describes a method for converting a monolithic application into a microservice architecture of a container-based distributed system. Microservices are defined through data analysis of a monolithic application. It also provides a method for creating templated deployment scripts to support and distribute microservices in a containerized environment. The results of the study confirmed the effectiveness of the proposed approach. A runtime performance evaluation for API calls at various stages of development was conducted, which confirmed the benefits of a microservice architecture. The findings show that microservices created by the proposed method have higher reusability compared to those created by traditional methods. Keywords: container, microservice, API, scalability, distributed system

A Systematic Literature Review of Cloud Brokers for Autonomic Service Distribution

A Systematic Literature Review of Cloud Brokers for Autonomic Service Distribution

Autonomous Services: The Evolution of Services Through Intelligent Vehicles

Autonomous Services: The Evolution of Services Through Intelligent Vehicles

Distributed Optimization for Autonomous Restoration in DER-Rich Distribution System

Autonomous service restoration (ASR) of active distribution network (ADN) reduces service restoration time with the help of measurement devices, smart switches and distributed energy resources (DERs) considering the system's operational and radiality constraints. Restoration scheme can be deployed in both centralized and distributed manner. However, with increasing data points, the requirement for measurement availability at the control center for the decision support makes centralized optimization challenging. The decomposition and coordination scheme of distributed algorithms enhances its ability to solve large scale optimization problems. Moreover, distributed optimization enables robustness, scalability and resiliency compared to centralized optimization. In this work, a) the distributed ASR problem is solved using a novel penalty-driven distributed alternating direction method of multipliers algorithm (PD-ADMM), b) a switch level decomposition and coordination technique is proposed, c) load restoration, DER utilization are considered directly and radiality of ADN is enforced using commodity flow model. The applicability of the algorithm is validated using modified IEEE 33-bus, IEEE 123-bus and 1069-bus test systems for contingency cases, communication failure, scalability, accuracy and sensitivity to number of partitions, starting points, change in DER / solar generation.

Understanding Reactions in Human‐Robot Encounters with Autonomous Quadruped Robots

ABSTRACTIncidental human‐robot encounters are becoming more common as robotic technologies proliferate, but there is little scientific understanding of human experience and reactions during these encounters. To contribute towards addressing this gap, this study applies Grounded Theory methodologies to study human reactions in Human‐Robot Encounters with an autonomous quadruped robot. Based upon observation and interviews, we find that participants' reactions to the robot can be explained by their attitudes of familiarity, certainty, and confidence during their encounter and by their understanding of the robot's capabilities and role. Participants differed in how and whether they utilized opportunities to resolve their unfamiliarity, uncertainty, or lack of confidence, shedding light on the dynamics and experiential characteristics of Human‐Robot Encounters. We provide an emerging theory that can be used to unravel the complexity of the field as well as assist hypothesis generation in future research in designing and deploying mobile autonomous service robots.

Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels

This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.

Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI

Generative AI technologies, including large language models (LLMs), have the potential to bring significant advancements to the tourism and hospitality industry through an array of ingenious features. ChatGPT, as a type of generative AI, is a state-of-the-art LLM that is bundled with extensive capabilities. This commentary provides an early snapshot of ChatGPT's ability to enhance the tourist decision-making process in the pre-trip, en-route, and post-trip stages. Particularly, ChatGPT greatly differentiates itself from the traditional travel decision-making process by putting the tourist in the driver's seat to actively probe for highly relevant information through a question-and-answer mode. In this research note, we present different scenarios with cases demonstrating how ChatGPT can improve tourists' experience in these three travel stages, with trip planning efficiency, customized recommendations, 24/7 personal assistant, AI-mediated communication fluency, autonomous guided tours, enriched sharing experience, and AI-induced prolonged satisfaction. In summary, these value-added autonomous services provide tourists with travel solutions that are highly cost-effective, offering customized information that allows them to travel with ease.

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

How to Design Autonomous Service Level Agreements for 6G

With the growing demand for network connectivity and diversity of network applications, one primary challenge that network service providers are facing is managing the commitments for Service Level Agreements (SLAs). Service providers typically monitor SLAs for management tasks such as improving their service quality, customer billing and future network planning. Network service customers, on their side, monitor services provided to them, to optimize their network usage and apply, when required, penalties related to service failures. In future 6G networks, critical network applications such as remote surgery and connected vehicles will require these SLAs to be more dynamic, flexible, and automated to match their diverse requirements on network services. Moreover, these SLAs should be transparent to all stakeholders to address the trustworthiness on network services and service providers required by critical applications. Currently, there is no standardized method to immutably record and audit SLAs, leading to challenges in aspects such as SLA enforcement and accountability - traits essential for future network applications. This work explores new requirements for future service contracts, that is, on the evolution of SLAs. Based on those new requirements, we propose an end to end layered SLA architecture leveraging Distributed Ledger Technology (DLT) and smart contracts. Our architecture is inheritable by an existing telco-application layered architectural frameworks to support future SLAs. We also discuss some limitations of DLT and smart contracts and provide several directions of future studies.

A Line Segment Detector for Space Target Images Robust to Complex Illumination

The relative pose estimation of the space target is indispensable for on-orbit autonomous service missions. Line segment detection is an important step in pose estimation. The traditional line segment detectors show impressive performance under sufficient illumination, while it is easy to fail under complex illumination conditions where the illumination is too bright or too dark. We propose a robust line segment detector for space applications considering the complex illumination in space environments. An improved two-dimensional histogram construction strategy is used to optimize the Otsu method to improve the accuracy of anchor map extraction. To further improve line segment detection’s effect, we introduce an aggregation method that uses the angle difference between segments, the distance between endpoints, and the overlap degree of segments to filter the aggregation candidate segments and connect disjoint line segments that probably came from the same segment. We demonstrate the performance of the proposed line segment detector using a variety of images collected on a semiphysical simulation platform. The results show that our method has better performance than traditional line segment detectors including LSD, Linelet, etc., in terms of line detection precision.

SWOT Matrix as a tool for diagnosing the municipal water supply system of a small city: a case study

Small municipalities often face challenges related to their water supply systems, such as limited financial resources, low population densities and limited organizational capacity. In this context, this study aimed to diagnose the water supply system in the city of Quatro Pontes - PR, which serves 2,628 economies, through SWOT analysis. This analysis consisted of determining the strengths, weaknesses, opportunities and threats of the system, listed based on reports from employees and visual inspections. It was found that the system presents factors already documented for small municipalities with autonomous services, such as the strengths “good water quality” and “easy maintenance”, as well as the weaknesses “few financial resources applied” and “outdated pricing policy”. In addition, opportunities were mentioned, such as technological innovations in the sector, and threats related to water waste and contamination by pesticides. The results presented depict the water supply system of the municipality and can direct managers to take advantage of opportunities and mitigate system threats.

Developing smart city services using intent‐aware recommendation systems: A survey

AbstractSmart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent‐aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision‐making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

An approach for real time management of global manufacturing enterprises based on Digital Data Stream

Advances in sensor technology, IOTs, communication technologies, computing, data management and analytics is changing the way modern global manufacturing enterprises are managed. With appropriate design and architecture of these technologies it is possible to use digital data streams to manage these enterprises in real time. This paper focuses on issues related to the design of these systems. Specifically, we focus on developing approaches for the design of autonomous services that receive data from local devices (e.g. sensors) in real time, do some processing in real time, communicate with other services, and respond to stimuli received from other services in real time. This allows localization of appropriate decision making. These services pass on relevant information to other related services in a web like architecture.

Anthropomorphic Robot Factors and Shoppers’ Acceptance of Autonomous Service Robots in Shopping Mall Complexes in Lagos State, Nigeria

This study examines the influence of anthropomorphic robot factors on shoppers’ acceptance of autonomous service robots in shopping mall complexes. It also contributes to the Service Robot Acceptance Model from the context of a developing country by validating three robot factors such as appearance, social capability and usefulness from shoppers’ perspectives.

Long-Term Localization Using Semantic Cues in Floor Plan Maps

Lifelong localization in a given map is an essential capability for autonomous service robots. In this letter, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.