Autonomous Service Research Articles

KeJia: towards an autonomous service robot with tolerance of unexpected environmental changes

KeJia is a domestic service robot, consisting of a mobile base, an arm, two cameras, and a set of software components for perception, manipulation, natural language understanding, motion and task planning, and decision making. With on-line running of these functions, a robot can adapt to dynamic environments which may have unexpected changes. In this paper, we propose a novel hierarchical method which combines motion planning with a neural network, so that the robot can tolerate errors from sensors, wear of parts, and human disturbances during motion execution. We evaluate our work on KeJia that cooks popcorn using a microwave oven, where humans try to disturb KeJia during the operation.

Navigating a Service Robot for Indoor Complex Environments

This paper investigates the use of an autonomous service robot in an indoor complex environment, such as a hospital ward or a retirement home. This type of service robot not only needs to plan and find paths around obstacles, but must also interact with caregivers or patients. This study presents a type of service robot that combines the image from a 3D depth camera with infrared sensors, and the inputs from multiple sonar sensors in an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based approach in path planning. In personal contacts, facial features are used to perform person recognition in order to discriminate between staff, patients, or a stranger. In the case of staff, the service robot can perform a follow-me function if requested. The robot can also use an additional feature which is to classify the person’s gender. The purpose of facial and gender recognition includes helping to present choices for suitable destinations to the user. Experiments were done in cramped but open spaces, as well as confined passages scenarios, and in almost all cases, the autonomous robots were able to reach their destinations.

Monitoring and Emergency Management in Autonomous Mobility Service: Investigating User Acceptance and Stated Preferences in Different Scenarios

Monitoring and Emergency Management in Autonomous Mobility Service: Investigating User Acceptance and Stated Preferences in Different Scenarios

The dynamic reconfiguration approach for fault-tolerance web service composition based on multi-level VCSOP

The dynamism of web service on the internet brings new needs to build autonomous services. In practical environments, Web services could occur many incidents with unacceptable information and generate a large number of undesirable solutions. Thus, it becomes an additionally urgent challenge on how to adjust service-based applications to meet highly dynamic environments and fast-changing business requirements. A self-reconfigurable application becomes a necessity to produce an adequate web service process. To cope with this hindrance, the present paper proposes a sensor and repair-based algorithm to achieve reliability at the component level before a system crash occurs. We adopt a new sensor method based on project management rules to detect the faulty region on a workflow. After that, we applied the repair algorithm: such as a single service (SSR) and multiple service reconfiguration (MSR) that systematically search to find a new nearby solution. In MSR algorithm we referred to a new multi-level VCSOP with Harmony Search (HS) metaheuristic to quickly recover the faulty region without system crash. The results show that our proposed method could recover the original instance of the faulty region without user integration and in a very acceptable time regarding the recomposing process.

Business Level Objectives of Customer for Autonomous Cloud Service Negotiation

Business Level Objectives of Customer for Autonomous Cloud Service Negotiation

Adaptive mobile Web server framework for Mist computing in the Internet of Things

PurposeThe distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks.Design/methodology/approachThis paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node.FindingsThe prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS.Originality/valueThe proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.

A Biologically Inspired Approach for Robot Depth Estimation

Aimed at building autonomous service robots, reasoning, perception, and action should be properly integrated. In this paper, the depth cue has been analysed as an early stage given its importance for robotic tasks. So, from neuroscience findings, a hierarchical four-level dorsal architecture has been designed and implemented. Mainly, from a stereo image pair, a set of complex Gabor filters is applied for estimating an egocentric quantitative disparity map. This map leads to a quantitative depth scene representation that provides the raw input for a qualitative approach. So, the reasoning method infers the data required to make the right decision at any time. As it will be shown, the experimental results highlight the robust performance of the biologically inspired approach presented in this paper.

Ground resource optimization for navigation constellation based on satellite link scheduling

ABSTRACTThe Global Navigation Satellite System (GNSS) provides autonomous geospatial positioning and time synchronization services for civilian and military purposes. GNSS satellite links are used to transmit signals for constellation management and other applications. To minimize the number of stations required in participant ground-based management and reduce management costs in the GNSS network, this study solves the satellite link scheduling problem over the dynamic satellite network, including the hybrid and fast-varying inter-satellite link (ISL) working system. Firstly, the management problem is divided into two phases. Secondly, a two-level solving scheme is designed according to the characteristics of the scheduling problem. Finally, a performance analysis of different scenarios is conducted. The experimental results show that the hybrid ISL working system provides more effectively utilized data transmitting bandwidth of satellite links, thereby reducing the use of ground-based management resources. This study provides a technical reference for the management of future large-scale satellite networks.

Knowledge-Based Autonomous Service Provisioning in Multi-Domain Elastic Optical Networks

Optimization of end-to-end service provisioning across multi-domain EONs is critically important due to the restricted intra-domain visibility and the agile spectrum allocation mechanism of EONs. Previously demonstrated schemes apply fixed service provisioning policies (e.g., heuristic routing and spectrum assignment and threshold- based anomaly detection) and fail to fully address the inherent principle and dynamic conditions of multi-domain EONs. This article brings in data analytics and presents a knowledge-based autonomous service provisioning framework for multi-domain EONs. The proposed framework employs a new broker plane working with domain managers while supporting the autonomy of each management domain. This can be seen as a hierarchical multi-domain network control and management architecture in support of enhanced resource efficiency and network scalability where each hierarchy employs an observe-analyze-act cycle. Specifically, the broker and domain managers can observe the network state through comprehensive network monitoring (monitoring of optical paths' performance, resource utilization, and traffic dynamics), analyze the network behavior and form the knowledge repository of network rules using machine learning techniques, and act intelligently according to the obtained knowledge. We assess the proposed framework by evaluating two key knowledge-based service provisioning use cases: proactive inter-domain traffic-prediction- based autonomous traffic engineering and quality-of-transmission-aware inter-domain lightpath provisioning. Numerical results demonstrate remarkable benefits of the proposed framework over conventional solutions.

Indoor robot localization combining feature clustering with wireless sensor network

Indoor robot localization is an indispensable ingredient for robots to perform autonomous services because GPS (Global Position System) information is not available. Natural features are usually used to implement this task, but it is difficult to solve the problem of localization robustness. A solution is proposed combining feature clustering and wireless sensor network to improve the effectiveness of robot localization: firstly, the SIFT (scalable invariable feature transform) features are extracted with feature clustering algorithm to estimate the robot position; secondly, the wireless sensor network is constructed to localize the robot from another independent way; finally, EKF (extended Kalman filter) is utilized to fuse the two kinds of localization results. The experiments demonstrate that this proposed method is effective and robust.

A Decentralized Technique for Autonomous Service Restoration in Active Radial Distribution Networks

This paper proposes a fully decentralized multi-agent system (MAS) based technique for service restoration of radial distribution networks. The technique utilizes expert system rules and considers the customers’ priority and the presence of distributed generators (DGs). It also considers the operational constraints in both healthy and restored sections of the feeder. The technique relies on one type of agents only, hence, simplifying its implementation. Moreover, it allows for assigning a back-up decision making agent to improve the reliability of the restoration process. The effectiveness of the technique is validated through several case studies simulated on an 11 kV distribution feeder. The agents are implemented in Java Agent Developing Framework (JADE) environment for communications and decisions making. Power flow calculations are performed in MATLAB environment to validate the correctness of the agents’ decisions.

INTERNET INTELLIGENT SERVICES OF VEHICLES BASED ON THE INTERNET OF THINGS AND MULTIAGENT SYSTEMS

The facilities offered by the distributed computing systems such as Internet of Things (IoT), multi-agent systems (MASs) andvehicular networks can be used to integrate such paradigms to provide several services of Internet of Vehicles (IoV). The coreobjective of this paper is to present the integration of Multi-agent Systems (MASs) with IoT and IoV as a new model in CypherPhysical System (CPS) of smart vehicles application. MASs are more effective when we employ them with distributed computinginfrastructures as with IoV to achieve more high performance. On the other hand, there is a lack of research works and a few papersin the literature till today with the integration of such paradigms, MAS, IoT, and IoV. The features of the proposed integration arebased on the main characteristics of intelligent agents like intelligence, flexibility, proactivity and reactivity, and autonomy whichcan be employed in IoV paradigm to provide new solutions and intelligent services that will explore new advances and differentfacilities in transportation problems. Also, IoT provides an adequate environment for communication with MASs paradigms andapplications as it provides reliable, flexible, effective, and smart services to vehicular networks. Moreover, integrating MASs withIoT and IoV could provide flexible, autonomous, and intelligent services of IoV. Some limitations and challenges of the proposedintegration method in this paper are presented with some future research trends.

A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots.

Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.

Building a cognitive platform for the managed IT services lifecycle

In this paper, we present IBM's cognitive strategy for service delivery transformation to a services integration approach for IT service management. This transformation is fueled by three fundamental technological paradigms: cognitive, cloud, and data and digital content. At the foundation of this approach is the IBM Services Platform with Watson—an IBM Cloud and Watson-based platform that uses machine learning, natural language, discovery, and various Watson application programming interfaces to design superior client solutions, achieve an exceptional level of autonomic service management, and facilitate a healthy always-on environment. It continuously learns and optimizes information technology performance to enable enhanced client business outcomes.

Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges

Social IoT (SIoT) is an emerging paradigm of IoT in which different IoT devices interact and establish relationships with each other to achieve a common goal. In essence, SIoT adapts a service-oriented architecture where heterogeneous IoT devices can offer or request autonomous services and collaborate on behalf of their owners. Operating Systems (OSs) are employed in IoT devices as they offer portability, threading support and access to development libraries; thus allowing easiness in IoT application development. Several OSs are available for IoT devices, but selecting an OS and hardware befitting for a particular IoT application is a critical task. In case of SIoT, the specific OS selection for hardware devices in various applications is even more challenging because of their collaborative nature. Existing surveys on OSs are mostly domain oriented and lack the discussion on hardware architectural features. As a consequence, it is infeasible for developers to choose best-suited OS for various hardware platforms which results in their underperformance in many application scenarios. This paper considers standard features of OS as well as hardware IoT platforms and provides an OS-to-hardware architectures features-mapping while exploring the unique requirements of SIoT applications. In doing so, resource-constrained IoT devices are particularly emphasized due to their memory constraints and power limitations. Further, a model OS architecture is proposed for devices in SIoT applications and associated open research challenges are identified. This research will benefit developers to best utilize IoT platform resources and to envisage an efficient OS for futuristic SIoT applications.

Autonomous Intelligent Robotic Navigation System Architecture With Mobility Service for IoT

This article presents the platform for autonomous vehicle architecture, navigation optimization and mobility services. The basic approach is to develop an intelligent agent to create a safety journey and redefine the world of transportation. The goal is to eliminate human driving errors and save human life from accidents. AI robots are a concept of future transportation with full automation and self-learning. Velodyne laser sensors are used for obstacle detection and autonomous navigation of ground vehicles and to create 3D images of the surround so that navigation and controls are optimized. In this article, existing system accessibility will be optimized by multiple features. The agent accessibility is improved, and users can access the vehicles through different ways like mobile apps, speech recognition and gestures. This article concentrates on the mobility services of autonomous vehicles.

Advanced mobile and wearable systems

The recent spectacular progress in the microelectronic, information, communication, material and sensor technologies created a big stimulus towards development of smart communicating cyber-physical systems (CPS) and Internet of Things (IoT). CPS and IoT are undergoing an explosive growth to a large degree related to advanced mobile systems like smart automotive and avionic systems, mobile robots and wearable devices. The huge and rapidly developing markets of sophisticated mobile cyber-physical systems represent great opportunities, but these opportunities come with a price of unusual system complexity, as well as, stringent and difficult to satisfy requirements of many modern applications. Specifically, smart cars and various wearable systems to a growing degree involve big instant data from multiple complex sensors or other systems, and are required to provide continuous autonomous service in a long time. In consequence, they demand a guaranteed (ultra-)high performance and/or (ultra-)low energy consumption, while requiring a high reliability, safety and security. To adequately address these demands, sophisticated embedded computing and embedded design technologies are needed. After an introduction to modern mobile systems, this paper discusses the huge heterogeneous area of these systems, and considers serious issues and challenges in their design. Subsequently, it discusses the embedded computing and design technologies needed to adequately address the issues and overcome the challenges in order to satisfy the stringent requirements of the modern mobile systems.

Editorial Column—New Directions in Service Science: Value Cocreation in the Age of Autonomous Service Systems

Editorial Column—New Directions in Service Science: Value Cocreation in the Age of Autonomous Service Systems

Caracterização de parâmetros físico – químicos e correlações com o manganês nos afluentes da barragem Santa Bárbara, Pelotas, RS

A Barragem Santa Bárbara localizada na cidade de Pelotas/RS possui quatro afluentes principais que por sua vez recebem despejos sanitários e industriais. A caracterização físico-química destas águas é importante para que o tratamento aplicado seja adequado e eficiente uma vez que são águas com fins de abastecimento público e que apresentam histórico de contaminação por excesso de manganês. Os objetivos deste trabalho foram caracterizar os principais afluentes da Barragem Santa Bárbara através de análises físico-químicas (pH, cloretos, oxigênio consumido, nitrogênio amoniacal, fósforo total, turbidez, demanda bioquímica de oxigênio e manganês total) e avaliar as correlações entre os parâmetros e o manganês. Para tal foram coletados dados físico – químicos nos registros do Serviço Autônomo de Saneamento de Pelotas (SANEP), referentes ao período de 2010 a 2015. Os parâmetros pH, cloretos, turbidez e oxigênio consumido estiveram de acordo com os padrões estabelecidos pela Resolução 357/05 do CONAMA; fósforo total, nitrogênio amoniacal, DBO5 e manganês total apresentaram resultados acima do permitido pela lei. O estudo das relações dos parâmetros físico-químicos com o Manganês mostrou haver correlações positivas e significativas entre Mn-Ptotal, no Epaminondas; Mn-Oconsumido no Epaminondas e na Sanga da Barbuda e Mn-Turbidez na Sanga da Barbuda, no Sítio Floresta e no Passo do Cunha.

The STRANDS Project: Long-Term Autonomy in Everyday Environments

Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance.