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Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique. Based on the unique functions of TMS, it has been widely used in clinical, scientific research and other fields. Nowadays, the robot-assisted automatic TMS has become the trend. In order to simplify the operation procedures of robotic TMS and reduce the costs, the purpose of this paper is to apply the marker-based augmented-reality technology to robotic TMS system.

By using the marker of ARToolKitPlus library and monocular camera, the patient’s head is positioned in real time. Furthermore, the force control is applied to keep contact between the coil and subject’s head.

The authors fuse with visual positioning which is based on augmented-reality and force-control technologies to track the movements of the patient’s head, bring the coil closer to the stimulation site and increase treatment effects. Experimental results indicate that the trajectory tracking control of robotic TMS system designed in this paper is practical and flexible.

This paper provides a trajectory tracking control method for the robotic TMS. The marker-based augmented-reality technology is implemented which simplifies the operation procedures of robotic TMS as well as reduce the costs. During the treatment process, the patients would wear an AR glasses, which can help patients relax through virtual scenes and reduce the uncomfortableness produce by treatment.

This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment.

First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems.

The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller.

A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

The purpose of this paper is to propose a novel jump control method based on Two Mass Spring Damp Inverted Pendulum (TMS-DIP) model, which makes the third generation of hydraulic driven wheel-legged robot prototype (WLR-3P) achieve stable jumping.

First, according to the configuration of the WLR, a TMS-DIP model is proposed to simplify the dynamic model of the robot. Then the jumping process is divided into four stages: thrust, ascent, descent and compression, and each stage is modeled and solved independently based on TMS-DIP model. Through WLR-3P kinematics, the trajectory of the upper and lower centroids of the TMS-DIP model can be mapped to the joint space of the robot. The corresponding control strategies are proposed for jumping height, landing buffer, jumping attitude and robotic balance, so as to realize the stable jump control of the WLR.

The TMS-DIP model proposed in this paper can simplify the WLR dynamic model and provide a simple and effective tool for the jumping trajectory planning of the robot. The proposed approach is suitable for hydraulic WLR jumping control. The performance of the proposed wheel-legged jump method was verified by experiments on WLR-3P.

This work provides an effective model (TMS-DIP) for the jump control of WLR-3P. The results showed that the number of landing shock (twice) and the pitch angle fluctuation range (0.44 rad) of center of mass of the jump control method based on TMS-DIP model are smaller than those based on spring-loaded inverted pendulum model. Therefore, the TMS-DIP model makes the jumping process of WLR more stable and gentler.

Because of the sharply growing interest worldwide of “hard” physical-mechanical robot systems for the execution of on-site construction tasks [i.e. single-task construction robots (STCRs)], the purpose of this study is to equip development projects with a systematic design-management system model that allows to integrate the different needs and aims of stakeholders.

This paper proposes a STCR-technology management system (STCR-TMS) for the complete development cycle of STCR designs. The STCR-TMS is based on established principles from systems engineering and management and STCR-specific activities developed and tested by the authors as standalone elements in previous research work.

The application of the STCR-TMS revealed the practicability of the method and the underlying concepts to provide practical guidance for the development process. Additional findings indicate that the method is sufficiently generic and flexible for application to different types of robots and indifferent world regions. This research has also shown that key activities need to be addressed to increase the practicability of the STCR-TMS.

A unique characteristic of this method is the evolution with each utilization cycle. In addition, individual elements are interchangeable and can be adapted based on external circumstances. These properties allow the TMS to be applied to other fields in construction robotics. With the progression of the verification and validation of the method, know-how and certain elements can be fed into standardization activities (e.g. establishing a management system standard).

This paper aims to report the collaboration between Microsoft and Tmsuk and describe the possible impact on the development of robotics software and the robot industry for the future.

The paper gives an insight into the collaboration, based on an interview with the CEO of Tmsuk.

Microsoft will be one step ahead in the robotic software standardization race. Tmsuk will evaluate the performance in the real world applications.

Tmsuk will evaluate Microsoft's Robotic Studio for its “practical” robotic applications and feed its findings back to Microsoft. The outcome is worth watching.

Microsoft Robotic Studio is the first robotic software development kit and Tmsuk is trying to prove it by its real world expertise. This is the first attempt to apply Micro Robotics Studio to practical applications in the real world.

The study aimed to examine the robotic process automation (RPA) contextual (center of excellence and scalability) and the multidisciplinary (TOE) determinants of RPA adoption in service industries in the emerging economy.

Ten factors were identified through literature surveys and popular studies grounded in technology, organization and environment. SPSS AMOS SEM is used for scale measurement and hypotheses testing. A sample of 313 respondents was collected from middle to above middle management executives of service industries from India. The authors tested the hidden layers and non-linear relationships using artificial neural network (ANN) analysis.

The low complexity, center of excellence (CoE), and industry/business partner pressure were significant to the RPA adoption in service industries in emerging economies. Counterintuitively, the scalability showed a negative influence on the RPA adoption, and the process capability did not show influence. The results of SEM and ANN were consistent.

This research can unfold the RPA adoption scholarly debate to multiple services industries beyond the telecom sector in emerging economies.

RPA is a disruptive technology on the artificial intelligence (AI) continuum. It has the potential to change the ways of working and enable technology-driven transformation. However, despite having thriving service industries that can benefit from RPA, emerging economies lag in adoption compared to the developed nations.

The RPA and automation can bring transformation to human society. Large economies such as India and China have large-scale demand for services, and the waiting lines are a common issue struggled by society. RPA can address the scalability issues of several services.

This study is among the first to examine technology-organization-environment (TOE) with RPA, including RPA contextual variables such as the CoE and scalability. Literature reports TOE applications on several emerging technologies of Industry 4.0 such as cloud, blockchain, big data and 3 Dimensional Printing (3DP), but no or little reported studies around RPA in services industries in emerging markets.

This paper reviews current advances in the development of Cyborgs and topics in systems and cybernetics. It includes reviews of: Biocybernetics – new applications of TMS, developments in nanotechnology, Cybernetics and automation reports, and research developments in Europe.

A general review and survey of selected research and development topics.

Illustrates the multi‐ and trans‐disciplinary nature of cybernetics and systems and aims to further research and development activity.

The choice of reviews provides an awareness of the current trends in these areas of endeavour.

The reviews are selected from a global database and give a studied assessment of current research and development initiatives.

Accurate perception of the environment using range sensors such as laser scanner, SONAR, infrared, vision, etc., for the application, such as path planning, localization, autonomous navigation, simultaneously localization and mapping, is a highly challenging area. The reliability of the perception by range sensors relies on the sensor accuracy, precision, sensor model, sensor registration, resolution, etc. Laser scanner is even though accurate and precise but still the efficient and consistent mapping of the environment is yet to be attained because laser scanner gives error as the extrinsic and intrinsic parameters varied which cause specular reflection, refraction, absorption, etc., of the laser beam. The paper aims to discuss this issue.

This paper presents an error analysis in sensory information of laser scanner due to the effect of varying the scanning angle with respect to the optical axis and surface reflectivity or refractive index of the targets. Uncertainties caused by these parameters are reduced by proposing a new technique, tilt mounting system (TMS) with designed filters of tilting the angular position of a laser scanner with the best possible selection of range and scanning angle for the robust occupancy grid mapping. Various experiments are performed in different indoor environments, and the results are validated after the implementation of the TMS approach with designed filters.

After the implementation of the proposed TMS approach with filters, the errors in the laser grid map are reduced by 15.6 percent, which results in 62.5 percent reduction in the collision of a mobile robot during autonomous navigation in the laser grid map.

The TMS approach with designed filter reduces the effect of variation in intrinsic and extrinsic parameters to generate efficient laser occupancy grid map to achieve collision-free autonomous navigation.

Business process modelling (BPMo) projects are core to digital transformation projects as they provide the conceptual foundation to the orchestration of technologies along a process. Yet, success factors of BPMo projects have received little research attention to date. This study empirically validates a BPMo project success model, pointing to normative practice guidelines and important future research.

This research reports on the quantitative re-specification and validation of a business process modelling success model, employing survey responses from 261 process modellers worldwide.

The study distilled and validated a final set of success antecedents: Top Management Support, Project Management Capabilities, Stakeholder Input, Modeller Expertise and Modelling Tool Usage; and clearly evidences their relationships with each other and with two moderating variables – Importance and Complexity. The paper offers a nuanced explanation of the indirect role of top management support in building stakeholder involvement and explains how complex projects that are perceived as important being more likely to attract high levels of necessary stakeholder involvement. The authors conclude that top management needs to create an environment of co-ordinated excellence which spans both technical skills and resources, and a high level of committed engagement between stakeholders and technicians in order for BPMo projects to succeed.

This study is the first to operationalize and quantitatively test antecedents of BPMo project success and their interactions, presenting novel insights into how the success factors interact. Awareness of the more influential antecedents of successful process modelling projects offers valuable guidance for the planning, management and conduct of BPMo projects.

The pressure to survive in a highly competitive market by using artificial intelligence (AI) has further demonstrated the need for automation in business operations during a crisis, such as COVID-19. Prior research finds managers' mixed perceptions about the use of technology in business, which underscores the need to better understand their perceptions of adopting AI for automation in business operations during COVID-19. Based on social exchange theory, the authors investigated managers' perceptions of using AI in business for effective operations during the COVID-19 pandemic.

This study collected data through a survey conducted in China (N = 429) and ran structural equation modeling to examine the proposed research model and structural relationships using Smart PLS software.

The results show that using AI in supply chain management, inventory management, business models, and budgeting are positively associated with managers' satisfaction. Further, the relationship between managers' satisfaction and effective business operations was found to be positively significant. In addition, the findings suggest that top management support and the working environment have moderating effects on the relationship between managers' satisfaction and effective business operations.

The results of this study can guide firms to adopt an AI usage policy and execution strategy, according to managers' perceptions and psychological responses to AI.

The study can be used to manage the behavior of managers within organizations. This will ultimately improve society's perception of the employment of AI in business operations.

The study's outcomes provide valuable insights into business management and information systems with AI application as a business response to any crisis in the future.