O. Reinoso, A. Gil, L. Payá and M. Juliá
This paper aims to present a teleoperation system that allows one to control a group of mobile robots in a collaborative manner. In order to show the capabilities of the…
Abstract
Purpose
This paper aims to present a teleoperation system that allows one to control a group of mobile robots in a collaborative manner. In order to show the capabilities of the collaborative teleoperation system, it seeks to present a task where the operator collaborates with a robot team to explore a remote environment in a coordinated manner. The system implements human‐robot interaction by means of natural language interfaces, allowing one to teleoperate multiple mobile robots in an unknown, unstructured environment. With the supervision of the operator, the robot team builds a map of the environment with a vision‐based simultaneous localization and mapping (SLAM) technique. The approach is well suited for search and rescue tasks and other applications where the operator may guide the exploration of the robots to certain areas in the map.
Design/methodology/approach
In opposition with a master‐slave scheme of teleoperation, an exploration mechanism is proposed that allows one to integrate the commands expressed by a human operator in an exploration task, where the actions expressed by the human operator are taken as an advice. In consequence, the robots in the remote environment choose their movements that improve the estimation of the map and best suit the requirements of the operator.
Findings
It is shown that the collaborative mechanism presented is suitable to control a robot team that explores an unstructured environment. Experimental results are presented that demonstrate the validity of the approach.
Practical implications
The system implements human‐robot interaction by means of natural language interfaces. The robots are equipped with stereo heads and are able to find stable visual landmarks in the environment. Based on these visual landmarks, the robot team is able to build a map of the environment using a vision‐based SLAM technique. SONAR proximity sensors are used to avoid collisions and find traversable ways. The robot control architecture is based on common object request broker architecture technology and allows one to operate a group of robots with dissimilar features.
Originality/value
This work extends the concept of collaborative teleoperation to the exploration of a remote environment using a team of mobile robots.
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Rafael Molina-Carmona, María Luisa Pertegal-Felices, Antonio Jimeno-Morenilla and Higinio Mora-Mora
Spatial ability is essential for engineers’ professional performance. Several studies describe it as a skill that can be enhanced using new technologies. Virtual reality (VR) is…
Abstract
Spatial ability is essential for engineers’ professional performance. Several studies describe it as a skill that can be enhanced using new technologies. Virtual reality (VR) is an emerging technology that is proving very useful for training different skills and improving spatial perception. In this chapter, the authors firstly present some previous works that use VR to train students, mainly in the area of engineering studies, and which demonstrate that VR can improve some aspects of the spatial perception. This study took a group of engineering students who used VR technologies to carry out learning activities designed to improve their spatial perception, which was measured with a widely used spatial ability test. The results obtained confirm that the use of VR technologies can improve students’ spatial perception. This proposal is easily transferable to other educational contexts. On the one hand, it could be implemented to improve spatial ability in other engineering studies, and on the other hand, with simple adaptation, it could be used to enhance other skills.
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Yanzhang Yao, Wei Wang, Yue Qiao, Zhihang He, Fusheng Liu, Xuelong Li, Xinxin Liu, Dehua Zou and Tong Zhang
The purpose of this paper is to describe the design and development of a novel series-parallel robot, which aims to climb on the transmission tower.
Abstract
Purpose
The purpose of this paper is to describe the design and development of a novel series-parallel robot, which aims to climb on the transmission tower.
Design methodology approach
This study introduces a hybrid robot, which consists of adsorption and two 3-degree of freedom (DOF) translation parallel legs connected by a body linkage. The DOF of the legs ensures that the robot can move on the climbing plane, also contribute to a compact design of the robot. An electromagnet is used to adsorb onto the transmission tower, simplifying the overall structure. Based on the robot design, this paper further defines its climbing gait and adopt the 6th B-spline curves for climbing trajectory planning under different working environments.
Findings
The developed prototype that implements the design of the robot, which was used in simulation and experiments, showing that the robot is capable of climbing in the test environments with the planned climbing gait.
Originality value
The hybrid robot is able to climb under varying degrees of inclinations and cross the obstacles, and the magnetic attraction can ensure stable climbing.
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Yanmei Xu, Zhenli Bai, Ziqiang Wang, Xia Song, Yanan Zhang and Qiwen Zhang
Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the…
Abstract
Purpose
Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the industrial internet. However, a gap persists in the existing research regarding the strategies and methods available to small and medium-sized enterprises (SMEs) for executing business model innovation. Therefore, this paper aims to explore the connotation, characteristics and logic of business model innovation for SMEs in the industrial internet era.
Design/methodology/approach
To explore the business model innovation logic of small and medium-sized enterprises in the era of industrial internet, the paper adopts a longitudinal single-case study approach, with PAYA, a medium-sized enterprise in the electromechanical industry, serving as the subject of research. It systematically analyzes PAYA’s business model innovation, centering on four key elements of the business model: value proposition, value creation, value delivery and value capture.
Findings
The study proposes two types of business model innovation, namely, “Migration” and “Expansion”, and explains the logic of business model innovation for SMEs in the industrial internet era: faced with a rapidly changing market environment, entrepreneurs put forward the value proposition through the insight of the market environment, then enterprises conduct technological innovation to support the value creation by their own unique experience and knowledge, and then improve the legitimacy of the market by expanding the influence of market acceptance of the new business model to promote the value delivery, and finally capture the economic value and ecological value.
Originality/value
The types and logic of business model innovation proposed in this paper contribute to supplementing and developing the theory of business model innovation and meanwhile have important reference value for SMEs in the industrial internet era.
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K.M. Ibrahim Khalilullah, Shunsuke Ota, Toshiyuki Yasuda and Mitsuru Jindai
The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.
Abstract
Purpose
The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.
Design/methodology/approach
Navigation in outdoor environments is still a challenging task for an autonomous mobile robot because of the highly unstructured and different characteristics of outdoor environments. This study examines a complete vision guided real-time approach for robot navigation in urban roads based on drivable road area detection by using deep learning. During navigation, the camera takes a snapshot of the road, and the captured image is then converted into an illuminant invariant image. Subsequently, a deep belief neural network considers this image as an input. It extracts additional discriminative abstract features by using general purpose learning procedure for detection. During obstacle avoidance, the robot measures the distance from the obstacle position by using estimated parameters of the calibrated camera, and it performs navigation by avoiding obstacles.
Findings
The developed method is implemented on a wheelchair robot, and it is verified by navigating the wheelchair robot on different types of urban curve roads. Navigation in real environments indicates that the wheelchair robot can move safely from one place to another. The navigation performance of the developed method and a comparison with laser range finder (LRF)-based methods were demonstrated through experiments.
Originality/value
This study develops a cost-effective navigation method by using a single camera. Additionally, it utilizes the advantages of deep learning techniques for robust classification of the drivable road area. It performs better in terms of navigation when compared to LRF-based methods in LRF-denied environments.
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Megha G. Krishnan, Abhilash T. Vijayan and Ashok S.
Real-time implementation of sophisticated algorithms on robotic systems demands a rewarding interface between hardware and software components. Individual robot manufacturers have…
Abstract
Purpose
Real-time implementation of sophisticated algorithms on robotic systems demands a rewarding interface between hardware and software components. Individual robot manufacturers have dedicated controllers and languages. However, robot operation would require either the knowledge of additional software or expensive add-on installations for effective communication between the robot controller and the computation software. This paper aims to present a novel method of interfacing the commercial robot controllers with most widely used simulation platform, e.g. MATLAB in real-time with a demonstration of visual predictive controller.
Design/methodology/approach
A remote personal computer (PC), running MATLAB, is connected with the IRC5 controller of an ABB robotic arm through the File Transfer Protocol (FTP). FTP server on the IRC5 responds to a request from an FTP client (MATLAB) on a remote computer. MATLAB provides the basic platform for programming and control algorithm development. The controlled output is transferred to the robot controller through Ethernet port as files and, thereby, the proposed scheme ensures connection and control of the robot using the control algorithms developed by the researchers without the additional cost of buying add-on packages or mastering vendor-specific programming languages.
Findings
New control strategies and contrivances can be developed with numerous conditions and constraints in simulation platforms. When the results are to be implemented in real-time systems, the proposed method helps to establish a simple, fast and cost-effective communication with commercial robot controllers for validating the real-time performance of the developed control algorithm.
Practical implications
The proposed method is used for real-time implementation of visual servo control with predictive controller, for accurate pick-and-place application with different initial conditions. The same strategy has been proven effective in supervisory control using two cameras and artificial neural network-based visual control of robotic manipulators.
Originality/value
This paper elaborates a real-time example using visual servoing for researchers working with industrial robots, enabling them to understand and explore the possibilities of robot communication.
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K.M. Ibrahim Khalilullah, Shunsuke Ota, Toshiyuki Yasuda and Mitsuru Jindai
Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot…
Abstract
Purpose
Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people.
Design/methodology/approach
A road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination.
Findings
The developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments.
Originality/value
The wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.
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SIA Increases In‐House Engineering Facilities at Paya Lebar Increasing in‐house engineering facilities and services at Paya Lebar have enabled Singapore Airlines to undertake a…
Abstract
SIA Increases In‐House Engineering Facilities at Paya Lebar Increasing in‐house engineering facilities and services at Paya Lebar have enabled Singapore Airlines to undertake a greater depth of aircraft engine maintenance at its home base. The latest engine type to be serviced in‐house is the Pratt & Whitney JT‐9D engine which powers the 747–200.
Miroslav Despotovic, David Koch, Eric Stumpe, Wolfgang A. Brunauer and Matthias Zeppelzauer
In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation…
Abstract
Purpose
In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.
Design/methodology/approach
The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.
Findings
The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.
Originality/value
To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.
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Kirti Nayal, Rakesh D. Raut, Maciel M. Queiroz, Vinay Surendra Yadav and Balkrishna E. Narkhede
This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural…
Abstract
Purpose
This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.
Design/methodology/approach
20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.
Findings
The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.
Research limitations/implications
This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.
Originality/value
This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.