Substantial capabilities of robotics in enhancing industry 4.0 implementation

Adaptive robotic system for the inspection of aerospace slat actuator mount

Introduction: Robotics uptake in the aerospace industry is low, mainly due to the low-volume/high-accuracy production that aerospace manufacturers require. Furthermore, aerospace manufacturing and assembly sites are often unstructured environments not specifically suitable for robots to operate in. Methods: This paper introduces a robotic visual inspection system using off-the-shelf components able to inspect the mounting holes for wing slat actuators without the need for fixed-coordinate programming; the part just needs to be left within reach of the robot. Our system sets one of the opposed pairs of mounting holes as a reference (the "datum") and then compares the tilt of all other pairs of mounting holes with respect to it. Under the assumption that any deviation in the mounting hole tilt is not systematic but due to normal manufacturing tolerances, our system will either guarantee the correct alignment of all mounting holes or highlight the existence of misaligned holes. Results and Discussion: Computer-vision tilt measurements are performed with an error of below 0.03° using custom optimization for the sub-pixel determination of the center and radius of the mounting holes. The error introduced by the robot's motion from the datum to each of the remaining hole pairs is compensated by moving back to the datum and fixing the orientation again before moving to inspect the next hole pair. This error is estimated to be approximately 0.05°, taking the total tilt error estimation for any mounting hole pair to be 0.08° with respect to the datum. This is confirmed by manually measuring the tilt of the hole pairs using a clock gauge on a calibrated table (not used during normal operation).

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker's direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot's surroundings.

How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation

To investigate how artificial intelligence (AI) affects the structure of labour force employment, we integrate robotics adoption and employment into this study's model. Based on Chinese provincial panel data from 2010 to 2019, fixed, mediating and threshold effects models and a spatial heterogeneity model were used to empirically test the impact of AI on the employment structure from the perspective of industrial structure optimisation and its mechanisms of action. The findings demonstrate that the impact of AI on the labour force employment structure reflects unique characteristics for China and promotes the advancement of the nation's employment structure. The influence of AI on the labour force employment structure follows a non-linear pattern, fostering labour force employment structure optimisation and upgrading from the perspective of industrial structure optimisation. Further investigation reveals the influence of spatial spillover effects from AI on employment structure optimisation. These research findings have theoretical value and practical significance for optimising China's employment structure in the context of AI.

Robotization of Industrial Processes: Motivational Differences between Companies with and without Existing Robotic Processes

OCCUPATIONAL APPLICATIONSOur survey of 100 manufacturing facilities revealed statistically significant differences among company types in their perceptions of cost savings, productivity gains, and safety improvements as benefits of robotic implementation. Regardless of company type or size, indications of presence of cumulative, incidental, and static postural hazards were identified as primary perceived factors for injury potential. More than half of the surveyed companies reported being unaware of general safety standards utilized within their facilities, and most (70%) robotic companies were unaware of any robotic-specific standards utilized at their company. Our results indicate the importance of accounting for varying perspectives between company types regarding motivation for and safety impacts of robotics. With the advancement of technology and robotization of the manufacturing industry, there are also pressing needs for advancing robotic safety standards, implementing training programs, and continuous promotion of the general safety awareness.

Human-Robot Collaboration for Healthcare: A Narrative Review

Robotic applications have often quickly transitioned from industrial to social. Because of this, robots can now engage with people in a natural way and blend in with their surroundings. Due to the lack of medical professionals, growing healthcare costs, and the exponential rise in the population of vulnerable groups like the ill, elderly, and children with developmental disabilities, the use of social robots in the healthcare system is expanding. As a result, social robots are employed in the medical field to entertain and educate hospitalized patients about health issues, as well as to assist the elderly and sick. They are also employed in the dispensing of medications, rehabilitation, and emotional and geriatric care. Thus, social robots raise the standard and effectiveness of medical care. This article explains how patients and healthcare professionals collaborate with robots in the healthcare industry. The objectives of this collaboration are to resolve moral and legal concerns, improve patient outcomes, and improve healthcare delivery. It has a broad range of uses, including telemedicine, rehabilitation, and robotic surgical support. Human-robot interaction is the term used to describe interactions between social robots and people. Many obstacles stand in the way of human-robot interaction in healthcare, including safety concerns, acceptability issues, appropriateness, usefulness, and the worry that robots may replace human carers. In the end, these difficulties result in a poor adoption rate for robotic technology. As a result, the applications and difficulties of human-robot interaction in healthcare are thoroughly evaluated in this research. This study also reviews future safety prospects from human-robot interaction in healthcare, as well as ethical and usability issues including privacy, trust, and safety, and our aims to provide a comprehensive overview of the use of robots in healthcare, including their applications, benefits, challenges, and prospects, to facilitate a deeper understanding of this evolving field.

The impact of industrial robot adoption on corporate green innovation in China

Green innovation plays a crucial role in transforming economic models and achieving sustainable development in enterprises. As an important embodiment of artificial intelligence technology, how industrial robots can effectively promote the green transformation of enterprises has become an important issue. This paper examines the impact and mechanisms of industrial robot adoption on corporate green innovation, as well as its heterogeneous effects. Using data from Chinese listed companies from 2007 to 2019, we find that industrial robot adoption has a significant positive impact on corporate green innovation, enhancing both its quantity and quality. Furthermore, our mechanism study reveals that industrial robot adoption can promote corporate green innovation by improving productivity and environmental management capabilities. Additionally, we investigate the moderating effects of various factors and conclude that the positive impact of industrial robot adoption on green innovation is more pronounced among the state-owned enterprises, enterprises with the intense market competition, as well as enterprises located in regions with higher carbon emissions intensity. This paper contributes to enrich the research on industrial robots and corporate green innovation, and provides a reference to improve environmental management and achieve a low-carbon economy in emerging markets.

Testing for quadratic impact of industrial robots on environmental performance and reaction to green technology and environmental cost

Industrial robots play a crucial role in enhancing productivity but their impact on the environment has produced debates. Some researchers have focused on the relation between industrial robots and energy efficiency (or environmental performance), such as Huang et al. (Energy Econ 107:105837, 2022) and Luan et al. (Sustain Prod Consum 30:870-888, 2022). However, their arguments mainly depend on the assumption of linear relationship between the two. This study infers that there is a nonlinear relationship between them from the theories of energy-saving effect, rebound effect, and scale effect. Our research, using data from 74 countries and regions worldwide between 1997 and 2020, reveals an inverted U-shaped relationship between the use of robots and their environmental impact. This means that the environment benefits from robot use up to a certain point, beyond which it starts to incur damage. Two moderating factors, green technology and environmental cost, are analyzed and tested. Our findings suggest that the high-green-tech left shifts and steepens the inverted U-shaped relationship whereas the high cost right shifts and flattens the relationship. This study explains the influencing mechanism of industrial robots on environmental performance by integrating the energy-saving effect, the rebound effect, and the scale effect. Our findings enrich the understanding of the robot-environment nexus and emphasize the importance of government in balancing robot use and environmental protection.

From Corrective to Predictive Maintenance-A Review of Maintenance Approaches for the Power Industry

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.

Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies

The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0 and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of “sustainability trilemma” a new term coined by the authors, and the reasoning for calling the next industrial revolution “Industry 4.0S” (another new term) rather than Industry 5.0 are also presented.

Analyzing the outcomes of a robotics workshop on the self-efficacy, familiarity, and content knowledge of participants and examining their designs for end-of-year robotics contests

Rapid advances in science and engineering, and pervasive adoption of resulting technological products, are influencing every aspect of human living and fueling a growing demand for a workforce that is adequately prepared for the emerging occupations in STEM fields. Educating students for success in the modern technology-rich workplace requires teachers who have the knowledge, comfort, capability, and training to adopt and integrate new technologies for classroom teaching and learning. Thus, to prepare high school teachers for incorporating robotics in their students' education and promoting their understanding of engineering concepts and technology applications, a four-week long robotics workshop was designed and conducted annually for three summers. Examination of changes in the workshop participants' levels of robotics self-efficacy, familiarity, and content knowledge, as well as analysis of outcomes of robotics capstone projects and end-of-year contests, is suggestive of study findings being promising for education researchers and professional development providers interested in leveraging the potential of robotics in STEM education.

Trajectory Optimization in Terms of Energy and Performance of an Industrial Robot in the Manufacturing Industry

Currently, the high demand for new products in the automotive sector requires large investments in factories. The automotive industry is characterized by high automatization, largely achieved by manipulator robots capable of multitasking. This work presents a method for the optimization of trajectories in robots with six degrees of freedom and a spherical wrist. The optimization of trajectories is based on the maximization of manipulability and the minimization of electrical energy. For this purpose, it is necessary to take into account the kinematics and dynamics of the manipulator in order to integrate an algorithm for calculation. The algorithm is based on the Kalman method. This algorithm was implemented in a simulation of the trajectories of a serial industrial robot, in which the robot has a sealer gun located on its sixth axis and the quality of the sealer application depends directly on the orientation of the gun. During the optimization of the trajectory, the application of the sealer must be guaranteed. This method was also applied to three different trajectories in the automotive sector. The obtained results for manipulability and electrical energy consumption prove the efficiency of the algorithm. Therefore, this method searches for the optimal solution within the limits of the manipulator and maintains the orientation of the final effector. This can be used for a known trajectory.

Design and Development of a Multi-Functional Bioinspired Soft Robotic Actuator via Additive Manufacturing

The industrial revolution 4.0 has led to a burst in the development of robotic automation and platforms to increase productivity in the industrial and health domains. Hence, there is a necessity for the design and production of smart and multi-functional tools, which combine several cutting-edge technologies, including additive manufacturing and smart control systems. In the current article, a novel multi-functional biomimetic soft actuator with a pneumatic motion system was designed and fabricated by combining different additive manufacturing techniques. The developed actuator was bioinspired by the natural kinematics, namely the motion mechanism of worms, and was designed to imitate the movement of a human finger. Furthermore, due to its modular design and the ability to adapt the actuator’s external covers depending on the requested task, this actuator is suitable for a wide range of applications, from soft (i.e., fruit grasping) or industrial grippers to medical exoskeletons for patients with mobility difficulties and neurological disorders. In detail, the motion system operates with two pneumatic chambers bonded to each other and fabricated from silicone rubber compounds molded with additively manufactured dies made of polymers. Moreover, the pneumatic system offers multiple-degrees-of-freedom motion and it is capable of bending in the range of −180° to 180°. The overall pneumatic system is protected by external covers made of 3D printed components whose material could be changed from rigid polymer for industrial applications to thermoplastic elastomer for complete soft robotic applications. In addition, these 3D printed parts control the angular range of the actuator in order to avoid the reaching of extreme configurations. Finally, the bio-robotic actuator is electronically controlled by PID controllers and its real-time position is monitored by a one-axis soft flex sensor which is embedded in the actuator’s configuration.

Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model

Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time. This paper proposes a prediction-based human-robot collaboration model for assembly scenarios. An embedded learning from demonstration technique enables the robot to understand various task descriptions and customized working preferences. A state-enhanced convolutional long short-term memory (ConvLSTM)-based framework is formulated for extracting the high-level spatiotemporal features from the shared workspace and predicting the future actions to facilitate the fluent task transition. This model allows the robot to adapt itself to predicted human actions and enables proactive assistance during collaboration. We applied our model to the seats assembly experiment for a scale model vehicle and it can obtain a human worker’s intentions, predict a coworker’s future actions, and provide assembly parts correspondingly. It has been verified that the proposed framework yields higher smoothness and shorter idle times, and meets more working styles, compared to the state-of-the-art methods without prediction awareness.

Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence

Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.

Resource Recycling with the Aim of Achieving Zero-Waste Manufacturing

The management of the huge amounts of waste generated from domestic and industrial activities has continued to be a source of concern for humanity globally because of its impact on the ecosystem and human health. Millions of tons of such used materials, substances, and products are therefore discarded, rejected, and abandoned, because they have no further usefulness or application. Additionally, owing to the dearth of affordable materials for various applications, the environmental impact of waste, and the high cost of procuring virgin materials, there have been intensive efforts directed towards achieving the reduction, minimization, and eradication of waste in human activities. The current review investigates zero-waste (ZW) manufacturing and the various techniques for achieving zero waste by means of resource recycling. The benefits and challenges of applying innovative technologies and waste recycling techniques in order to achieve ZW are investigated. Techniques for the conversion of waste glass, paper, metals, textiles, plastic, tire, and wastewater into various products are highlighted, along with their applications. Although waste conversion and recycling have several drawbacks, the benefits of ZW to the economy, community, and environment are numerous and cannot be overlooked. More investigations are desirable in order to unravel more innovative manufacturing techniques and innovative technologies for attaining ZW with the aim of pollution mitigation, waste reduction, cost-effective resource recovery, energy security, and environmental sustainability.

Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping

Instance segmentation of overlapping plants to detect their grasps for possible robotic grasping presents a challenging task due to the need to address the problem of occlusion. We addressed the problem of occlusion using a powerful convolutional neural network for segmenting objects with complex forms and occlusions. The network was trained with a novel dataset named the “occluded plants” dataset, containing real and synthetic images of plant cuttings on flat surfaces with differing degrees of occlusion. The synthetic images were created using the novel framework for synthesizing 2D images by using all plant cutting instances of available real images. In addition to the method for occlusion handling for overlapped plants, we present a novel method for determining the grasps of segmented plant cuttings that is based on conventional image processing. The result of the employed instance segmentation network on our plant dataset shows that it can accurately segment the overlapped plants, and it has a robust performance for different levels of occlusions. The presented plants’ grasp detection method achieved 94% on the rectangle metric which had an angular deviation of 30 degrees and an IoU of 0.50. The achieved results show the viability of our approach on plant species with an irregular shape and provide confidence that the presented method can provide a basis for various applications in the food and agricultural industries.

Mobile Robot Path Planning Based on Enhanced Dynamic Window Approach and Improved A ∗ Algorithm

Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A ${}^{\ast }$ algorithm with an enhanced dynamic window method is proposed. In the improved A ${}^{\ast }$ algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments.

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture.

Novel Insights in the Leadership in Business and Economics: A Post-Coronavirus Update

Our world is evolving at an incredibly enormous speed and what was impossible three years ago is now a reality. The concept of leadership and leaders has also undergone profound transformations. Moreover, the recent COVID-19 pandemic caused a digital surge in the ways economic life, business, or education are perceived or conducted. The pandemic proved that small and large businesses, industries, and the whole economies can be suddenly upended by massive technological shifts. Hence, there is a need for a theoretical research update in leadership in business and economics that would bring new insights into this topic and define its place within the context of Sustainable Development Goals (SDGs). Our paper presents the novel insights for the leaders and the leadership concept in business and economics from various approaches and angles of view in the light of the COVID-19 pandemic with a focus on sustainable leadership and organizational resilience. It aims at outlying the theoretical background of leadership in business and economics after the pandemic and bringing up interesting and recent leadership case studies from all around of the world. Moreover, this paper aims as sharing the valuable insights into what it means to be a sustainable leader in business and economics, why leaders are needed, and how to become one. The main criteria of this research and its instrumentation include both the theoretical discussion based on the literature review and analysis and the empirical analysis that supports these theoretical provisions. The paper features an empirical model that assesses how business and economic leaders are searching for new ways of work and personal development during and after the COVID-19 pandemic using the own data from the 400 respondents collected in the Czech Republic and Russia. We found that the pandemic enhanced the emotional creativity of business and economic leaders and made them to invest and engage more into using new digital technologies and fundamentally altering the old ways of managing and governing their respective companies and institutions. Our results might provide valuable food for thought both for academics working on various angles of leadership, as well as for entrepreneurs and businesspeople who want to receive recent updates on the topic of leadership to use them in their daily work.

Telemedicine for healthcare: Capabilities, features, barriers, and applications

Regular hospital visits can be expensive, particularly in rural areas, due to travel costs. In the era of the Covid-19 Pandemic, where physical interaction becomes risky, people prefer telemedicine. Fortunately, medical visits can be reduced when telemedicine services are used through video conferencing or other virtual technologies. Thus, telemedicine saves both the patient's and the health care provider time and the cost of the treatment. Furthermore, due to its fast and advantageous characteristics, it can streamline the workflow of hospitals and clinics. This disruptive technology would make it easier to monitor discharged patients and manage their recovery. As a result, it is sufficient to state that telemedicine can create a win-win situation. This paper aims to explore the significant capabilities, features with treatment workflow, and barriers to the adoption of telemedicine in Healthcare. The paper identifies seventeen significant applications of telemedicine in Healthcare. Telemedicine is described as a medical practitioner to diagnose and treat patients in a remote area. Using health apps for scheduled follow-up visits makes doctors and patients more effective and improves the probability of follow-up, reducing missing appointments and optimising patient outcomes. Patients should have an accurate medical history and show the doctor any prominent rashes, bruises, or other signs that need attention through the excellent quality audio-video system. Further, practitioners need file management and a payment gateway system. Telemedicine technologies allow patients and doctors both to review the treatment process. However, this technology supplements physical consultation and is in no way a substitute for a physical consultation. Today this technology is a safe choice for patients who cannot go to the doctor or sit at home, especially during a pandemic.

Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.