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The purpose of this paper is to achieve a very accurate localization of hidden metallic objects in human medicine applications.

The proposed methodology takes advantage of the eddy current effect within a metallic object. Its magnetic reaction field will be measured, e.g. with giant magnetic resistor (GMR) sensors.

A comparison of measurements and numerical results obtained by finite element computations demonstrate the reliability and positively gives a clue about the feasibility of the suggested method.

While measuring noisy signals, the use of a lock‐in amplifier is rather expensive; especially, in applications with a high number of GMR sensors the use of channel multiplexer must be considered, which again may generate noise.

The paper shows how appropriate shielding of external fields in the measurement setup ensures results of satisfying quality.

The purpose of this paper is to design and implement a team of kid size humanoid soccer robots conforming to RoboCup Humanoid league.

The project is described in two main parts: hardware and software. The hardware section consists of the mechanical structure and the driver circuit board enabling each robot to walk, fast walk, autonomously get up, kick and dribble when it catches the ball. The software is developed as a robot application which consists of motion controller, autonomous motion robot, self localization based on vision system, AI, trajectory planning and network.

This year, the authors' developments for the humanoid robot include: the design and construction of our new humanoid robots structure and implementation of a new recurrent hybrid neural network for walking control. The control system consists of two neural network controllers, two standard PD controllers and a robot walking planar. The proposed neural network controller has three layers, which are input, hidden, and output layers.

This paper presents results of research work in the field of autonomous robot‐middle size soccer robot, supported by IAU‐ Isfahan Branch (Khorasgan).

The purpose of this paper is to address the online localization of mobile (service) robots in real world dynamic environments. Most of the techniques developed so far have been designed for static environments. What is presented here is a novel technique for mobile robot localization in quasi‐dynamic environments.

The proposed approach employs a probability grid map and Baye's filtering techniques. The former is used for representing the possible changes in the surrounding environment which a robot might have to face.

Simulation and experimental results show that this approach has a high degree of robustness by taking into account both sensor and world uncertainty. The methodology has been tested under different environment scenarios where diverse complex objects having different sizes and shapes were used to represent movable and non‐movable entities.

The results can be applied to diverse robotic systems that need to move in changing indoor environments such as hospitals and places where people might require assistance from autonomous robotic devices. The methodology is fast, efficient and can be used in fast‐moving robots, allowing them to perform complex operations such as path planning and navigation in real time.

What is proposed here is a novel mobile robot localization approach that enables unmanned vehicles to effectively move in real time and know their current location in dynamic environments. Such an approach consists of two steps: a generation of the probability grid map; and a recursive position estimation methodology employing a variant of the Baye's filter.

The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization.

Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved.

The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks.

The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one.

The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

The Operations and Maintenance (O&M) cost of a facility is typically 60–85% of the total life cycle cost of a building whereas its design and construction cost accounts for only 5–10%. Therefore, enhancing and optimizing the O&M of a facility is a crucial issue. In addition, with the increasing complexities in a building's operating systems, more technologically advanced solutions are required for proactively maintaining a facility. Thereby, a tool is needed which can optimize and reduce the cost of facility maintenance. One of the solutions is Augmented or Mixed Reality (AR/MR) technologies which can reduce repair time, training time and streamline inspections. Therefore, the purpose of this study is to establish contextual knowledge of AR/MR application in facilities operation and maintenance and present an implementation framework through the analysis and classification of articles published between 2015 and 2022.

To effectively understand all AR/MR applications in facilities management (FM), a systematic literature review is performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed for searching and describing the search strategies. Keywords were identified through the concept mapping technique. The Scopus database and Google Scholar were employed to find relevant articles, books and conference papers. A thorough bibliometric analysis was conducted using VOS Viewer and subsequently, a thematic analysis was performed for the selected publications.

The use of AR/MR within facilities O&M could be categorized into five different application areas: (1) visualization; (2) maintenance; (3) indoor localization and positioning; (4) information management and (5) indoor environment. After a thematic analysis of the literature, it was found that maintenance and indoor localization were the most frequently used research application domains. The chronological evolution of AR/MR in FM is also presented along with the origin of publications, which showed that the technology is out of its infancy stage and is ready for implementation. However, literature showed many challenges hindering this goal, that is (1) reluctance of the organizational leadership to bear the cost of hardware and trainings for the employees, (2) Lack of BIM use in FM and (3) system lagging, crashing and unable to register the real environment. A preliminary framework is presented to overcome these challenges.

This study accommodates a variety of application domains within facilities O&M. The publications were systematically selected from the existing literature and then reviewed to exhibit various AR/MR applications to support FM. There have been no literature reviews that focus on AR and/or MR in the FM and this paper fills the gap by not only presenting its applications but also developing an implementation framework.

The purpose of this paper is to develop an automated industrial robotic system for handling steel castings of various sizes and shapes in a foundry.

The authors first designed a prismatic gripper for pick-and-place operations that incorporates underactuated passive hydraulic contact (PHC) phalanges that enable the gripper to easily adapt to different casting shapes. The authors then optimized the gripper parameters and compared it to an adaptive revolute gripper using two methods: a planar physics based quasistatic simulation that accounts for object dynamics and validation using physical prototypes on a physical robot.

Through simulation, the authors found that an optimized PHC gripper improves grasp performance by 12 per cent when compared to an human-chosen PHC configuration and 60 per cent when compared to the BarrettHand™. Physical testing validated this finding with an improvement of 11 per cent and 280 per cent, respectively.

This paper presents for the first time optimized prismatic grippers which passively adapt to an object shape in grasping tasks.

The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.

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.

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.

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.

The main purpose of this study is to present an overview of the state of the art of the RFID technology in terms of data storage approaches in construction cases, and to identify the factors that require different approaches to data storage (e.g. on tags or on a remote database) in RFID applications in the construction industry.

A literature survey was conducted and the contexts of 37 construction industry cases were investigated to determine the factors that affect the decision of data storage approach and the types of information groups that were stored in each case. Additionally, 79 cases were reviewed from other industries to provide insights.

The literature review showed that, many cases in the construction industry preferred storing additional data on RFID tags such as identification, technical and historical information. The factors affecting the selection of data storage approach in RFID applications were identified: application environment, cost efficiency, multiple number of parties, need for monitoring up‐to‐date progress data, collecting environmental conditions, in situ (on‐board) data storage, industry‐related specifications, and reading range requirement.

The high proportion of cases which stored data on tags demonstrate that there is a need for tags/storage media that are specially designed for the construction industry because most tags currently have either minimum or limited memories.

The analysis of the investigated cases and the factors that were identified to be affecting the data storage approach decision making can assist construction practitioners and owners in selecting an appropriate data storage approach for their projects.

This paper aims to uncover how implementation practices affect the unfolding of integration as the ideal of an enterprise resource planning (ERP) system. In this way, the authors aim to provide a better understanding of the ways in which the ideal of integration affects the complexity of ERP systems.

This paper interprets a case study of the implementation of an ERP system in a Dutch company.

The paper highlights how different variations of an object of integration were enacted during an ERP implementation. The authors observe how the interests of the various actors were not always served by the variations of integration in circulation. They illustrate how this resulted in a failure of the network to be folded into a taken-for-granted configuration of constituent parts. Consequentially, having multiple variations of integration contributed to increased complexity of the system.

The paper highlights how multiple variations of a single object of integration are brought into circulation. Such perspective enables a better understanding of some of the complexities associated with ERP implementations.