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In this work, we present a novel design for an embedded Radio Frequency (RF) antenna array that can distinguish various materials by analyzing changes in Received Signal Strength Indicator (RSSI) values. The use of a low-cost and small-form-factor microcontroller by Espressif makes this design both cost-effective and suitable for integration into various applications, differentiating it from previous studies. To enhance the material classification performance, a combination of Kalman filter and Support Vector Machine is proposed which does not require a large amount of training data for model optimization. Results demonstrate that the proposed machine learning model is able to perform material classification within a 2 m range, with an average accuracy of over 96%. Such a system is well-suited for intelligent mobile robotic applications particularly in warehouse automation or smart manufacturing lines due to its ability for proximal remote sensing, real-time monitoring, and multimodal sensing. [ABSTRACT FROM AUTHOR]

The intelligent soft robot has received wide attention from both academia and the industry due to its remarkable adaptability. It performs intelligent behavioral learning and evolved morphologies in unpredictable environmental conditions. However, designing a soft robot with a well-adapted morphology involves searching through a large number of possible structures. Furthermore, to learn control tasks in diverse environments, the robot performs computationally intensive numerical simulations, which is time-consuming for evaluating the performance of robots. To address both issues, a multi-fidelity evolutionary algorithm is proposed, which consists of three main components. Firstly, a niching-based fidelity adjustment strategy is introduced to significantly reduce the evaluation cost by training the controller of each robot for only a small number of simulation steps. In particular, considering the estimation errors of the low-fidelity evaluation, the population is divided into multiple subpopulations with different fidelity levels for parallel optimization. Secondly, an effective morphology transfer strategy is proposed to improve the quality of offspring by transferring the local structure of robots in different subpopulations. Finally, a fast local search is developed to enhance the search efficiency of the algorithm without performing additional control simulations. The experimental results on 31 test tasks demonstrate that the proposed algorithm outperforms the SOTA design algorithms on 25 test tasks, especially when the computational budget is limited. Compared to the baseline algorithms, our algorithm reduces the computational cost by 60$\%$% while achieving similar performance. [ABSTRACT FROM AUTHOR]

This research examines the use of programmed intelligent robots for the assembly process of supercomputers (high-performance computers - HPCs), drawing on multiple and credible/reputable scientific sources, highlighting the impact on the efficiency and quality of this industrial production, flawlessly achieved by handling and assembling the complex components of supercomputers (HPCs), once with reducing errors and improving the accuracy of this complex manufacturing process. The clear advantages of using programmed robots for supercomputer assembly are highlighted in this paper, such as: optimizing production time and reducing labor costs, significant potential to improve production efficiency and quality. [ABSTRACT FROM AUTHOR]

Traditional information retrieval algorithms ignore user needs and are unable to obtain user gaze coordinates and gaze times, resulting in low retrieval accuracy. The author proposes a new interactive information retrieval algorithm for this purpose. Divide eye tracking technology evaluation indicators, visually process eye movement information, obtain user gaze coordinates and gaze time, and calculate the influence coefficients of each gaze area and each point in the area. Weighted visual words are accumulated to get a visual word list with the weight of the associated area, and visual word list and Rocchio algorithm are combined to build a hidden Relevance feedback retrieval model in semantic space to judge information retrieval preferences. Intelligent robot is introduced, and Jensen Shannon divergence is used to calculate the Kullback--Leibler divergence distance between the probability distributions of document sets, calculate the similarity matching, and complete the interactive information retrieval. The simulation results prove that, due to the introduction of intelligent robot strategy in this method, the amount of irrelevant retrieval information is reduced by matching user needs and retrieval results. Therefore, the amount of messages generated by information retrieval is significantly lower than that of the two literature methods, without adding additional network load. The network system is in a stable operation state, and users can quickly grasp their own required information, and the retrieval speed is also improved to a certain extent. It is proved that the proposed algorithm has high retrieval accuracy, can effectively reduce network load and achieve high-quality human-computer interactive information retrieval. [ABSTRACT FROM AUTHOR]

In order to solve the problem of duplicate data entry between construction management platform systems in construction engineering, the author proposes to apply RPA intelligent process robots to replace manual data collection, operation, entry, and verification. The design of the system is divided into overall architecture, instruction program loading process, human-machine interaction system level services, and other levels. An end-to-end procedural instruction transmission control method is adopted, establish a low-level control command output module for the online calibration system of the flight path, using a basic service architecture system, implement human-machine interactive control of the online calibration system for flight paths on the B/S architecture system. Build a record controller module for the inspection trajectory correction of RPA intelligent process robots, and perform feedback control during the trajectory correction process in LOG-CONTROL-BLOCK, using the RPA feedback correction algorithm, achieve adaptive correction and error feedback tracking of the inspection trajectory of RPA intelligent process robots. Implement calibration system development and design in an integrated DSP (Digital Signal Processing) information processing platform. The experimental results show that good economic benefits have been achieved through application, and the problem of duplicate data entry between the employer's IFS system and the ENPOWER document management system has been solved, greatly reducing error rates and personnel costs. It can replace manual data collection, entry, verification, and business operations; It has the characteristics of low error rate, low cost, high accuracy, compliance, and 24/7 standby; In 2021, 108000 yuan was saved, in 2022, 432000 yuan was saved, and in 2023, 432000 yuan was saved, demonstrating the labor hour cost savings achieved by utilizing RPA intelligent process robots. [ABSTRACT FROM AUTHOR]

In this work, we present a novel design for an embedded Radio Frequency (RF) antenna array that can distinguish various materials by analyzing changes in Received Signal Strength Indicator (RSSI) values. The use of a low-cost and small-form-factor microcontroller by Espressif makes this design both cost-effective and suitable for integration into various applications, differentiating it from previous studies. To enhance the material classification performance, a combination of Kalman filter and Support Vector Machine is proposed which does not require a large amount of training data for model optimization. Results demonstrate that the proposed machine learning model is able to perform material classification within a 2 m range, with an average accuracy of over 96%. Such a system is well-suited for intelligent mobile robotic applications particularly in warehouse automation or smart manufacturing lines due to its ability for proximal remote sensing, real-time monitoring, and multimodal sensing.

The increasing prevalence of chronic diseases globally poses major challenges to the health of societies and individuals. Managing chronic diseases requires long-term treatment and monitoring, placing demands on patients' lifestyles. With the aging of the population and changes in lifestyle, chronic disease prevention and control are becoming more and more important. In recent years, as scientific and technological innovation in the field of healthcare develops in depth, and the application of artificial intelligence in healthcare has gradually become one of the important strategic directions of the country, the traditional method of chronic disease management relies too much on the offline communication between the doctor and the patient, which leads to the doctor not being able to maintain long-term and effective communication and follow up with the patient, and the patient may not be able to be detected and monitored by the doctor in a timely manner when his or her condition changes. In addition, the traditional chronic disease management approach is usually a generalized approach that fails to adequately consider the individual differences of each patient. Given the limitations of traditional chronic disease management methods, this study aims to provide more convenient and efficient primary care services using intelligent robots. Through personalized health management plans, assisted medical diagnosis, and timed medication reminders, the intelligent robot is committed to improving patients' quality of life, reducing the pressure on healthcare resources, and promoting the development of intelligent healthcare management globally.

Abstract Laminectomy is one of the most common posterior spinal operations. Since the lamina is adjacent to important tissues such as nerves, once damaged, it can cause serious complications and even lead to paralysis. In order to prevent the above injuries and complications, ultrasonic bone scalpel and surgical robots have been introduced into spinal laminectomy, and many scholars have studied the recognition method of the bone tissue status. Currently, almost all methods to achieve recognition of bone tissue are based on sensor signals collected by high‐precision sensors installed at the end of surgical robots. However, the previous methods could not accurately identify the state of spinal bone tissue. Innovatively, the identification of bone tissue status was regarded as a time series classification task, and the classification algorithm LSTM‐FCN was used to process fusion signals composed of force and cutting depth signals, thus achieving an accurate classification of the lamina bone tissue status. In addition, it was verified that the accuracy of the proposed method could reach 98.85% in identifying the state of porcine spinal laminectomy. And the maximum penetration distance can be controlled within 0.6 mm, which is safe and can be used in practice.

Laminectomy is one of the most common posterior spinal operations. Since the lamina is adjacent to important tissues such as nerves, once damaged, it can cause serious complications and even lead to paralysis. In order to prevent the above injuries and complications, ultrasonic bone scalpel and surgical robots have been introduced into spinal laminectomy, and many scholars have studied the recognition method of the bone tissue status. Currently, almost all methods to achieve recognition of bone tissue are based on sensor signals collected by high‐precision sensors installed at the end of surgical robots. However, the previous methods could not accurately identify the state of spinal bone tissue. Innovatively, the identification of bone tissue status was regarded as a time series classification task, and the classification algorithm LSTM‐FCN was used to process fusion signals composed of force and cutting depth signals, thus achieving an accurate classification of the lamina bone tissue status. In addition, it was verified that the accuracy of the proposed method could reach 98.85% in identifying the state of porcine spinal laminectomy. And the maximum penetration distance can be controlled within 0.6 mm, which is safe and can be used in practice. [ABSTRACT FROM AUTHOR]

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in clutter. To effectively perform grasping and pushing manipulations, robots need to perceive the position information of objects, including the coordinates and spatial relationship between objects (e.g., proximity, adjacency). The authors propose an end‐to‐end position‐aware deep Q‐learning framework to achieve efficient collaborative pushing and grasping in clutter. Specifically, a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high‐quality affordance maps of operating positions with features of pushing and grasping operations. In addition, the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial relationships between objects in cluttered environments. To further enhance the perception capacity of position information of the objects, the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function. A series of experiments are carried out in simulation and real‐world which indicate that the method improves sample efficiency, task completion rate, grasping success rate and action efficiency compared to state‐of‐the‐art end‐to‐end methods. Noted that the authors' system can be robustly applied to real‐world use and extended to novel objects. Supplementary material is available at https://youtu.be/NhG\_k5v3NnM}{https://youtu.be/NhG\_k5v3NnM. [ABSTRACT FROM AUTHOR]

Abstract Humanoid robots have attracted much attention by virtue of their compatibility with human environments. However, biped humanoids with immense promise still cannot function steadily and reliably in real‐world settings in the current state. Hence, rationally combining a humanoid robot with different stable mobile platforms is a favoured solution for diverse scenarios. Here, a new versatile humanoid robot platform, aiming to provide a generic solution that can be flexibly deployed in diverse scenarios, for example, indoors and fields is presented. Versatile humanoid robot platform incorporates multimodal perception, and extensible interfaces on hardware and software, allowing it to be rapidly integrated with different mobile platforms and end‐effectors, only through easy‐to‐assemble interfaces. Additionally, the platform has achieved impressive integration, lightness, dexterity, and strength in its class, with human‐like size and rich perception, targeted to have human‐intelligent manipulation skills for human‐engineered environments. Overall, this article elaborates on the reasoning behind the design choices, and outlines each subsystem. Lastly, the essential performance of the platform is successfully demonstrated in a set of experiments with precise and dexterous manipulation, and human–robot collaboration requirements.

In order to improve agricultural efficiency, reduce labor intensity, and improve the quality and yield of agricultural products on the Tibetan Plateau, this paper takes the application of traditional agricultural robots as an example and proposes the application of agricultural robots to agriculture on the Qinghai-Tibet Plateau based on the special breeding environment of the Qinghai-Tibet Plateau combined with the current situation of traditional agriculture. Through the application practice of robots in traditional agriculture, the possibility of the application of agricultural robots in the Qinghai-Tibet Plateau is pointed out, the working methods and advantages of robots in the Qinghai-Tibet Plateau are expounded, and the application of intelligent agricultural robots in the agriculture of the Qinghai-Tibet Plateau is discussed and analyzed. The results show that with the progress of science and technology, intelligent robots have gradually played an important role in various fields, and intelligent robots have broad application prospects in plateau agriculture. The application of agricultural robots in the Qinghai-Tibet Plateau should be aimed at the Qinghai-Tibet Plateau to make some adaptive research and development, intelligent upgrading and demonstration project promotion in order to promote the Qinghai-Tibet Plateau agricultural automation. [ABSTRACT FROM AUTHOR]

Online public opinion incidents in universities occur from time to time, with the source difficult to find, rapid dissemination, and escalating fragmentation. There are many problems and difficulties in the guidance of online public opinion in universities, and a new way out needs to be explored. This article explores the influencing factors of intelligent robots on online education in universities in the all-media era. This article believes that the ecology of university online education is an inherent component of university online culture and is the result of the interaction and mutual restraint between various elements and external environmental factors. The self-regulation of each element gradually tends towards a harmonious and stable state. It is closely related to cultural ecology, network cultural ecology, and university network culture, and also has its own unique characteristics. Establish a collaborative governance model between university public opinion work alliances and society, improve the application standards of artificial intelligence and the management mechanism of IGM and MGC technologies. Compared with traditional technologies, intelligent robots in the all-media era can guide the network public opinion of colleges and universities in the intelligent Internet era. [ABSTRACT FROM AUTHOR]

Humanoid robots have attracted much attention by virtue of their compatibility with human environments. However, biped humanoids with immense promise still cannot function steadily and reliably in real‐world settings in the current state. Hence, rationally combining a humanoid robot with different stable mobile platforms is a favoured solution for diverse scenarios. Here, a new versatile humanoid robot platform, aiming to provide a generic solution that can be flexibly deployed in diverse scenarios, for example, indoors and fields is presented. Versatile humanoid robot platform incorporates multimodal perception, and extensible interfaces on hardware and software, allowing it to be rapidly integrated with different mobile platforms and end‐effectors, only through easy‐to‐assemble interfaces. Additionally, the platform has achieved impressive integration, lightness, dexterity, and strength in its class, with human‐like size and rich perception, targeted to have human‐intelligent manipulation skills for human‐engineered environments. Overall, this article elaborates on the reasoning behind the design choices, and outlines each subsystem. Lastly, the essential performance of the platform is successfully demonstrated in a set of experiments with precise and dexterous manipulation, and human–robot collaboration requirements. [ABSTRACT FROM AUTHOR]

Abstract The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

The development of intelligent robot has always been an important research direction in the field of artificial intelligence, and the object detection of robot is the basis of intelligent perception and autonomous action. This study proposes an improved object detection algorithm which integrates two kinds of intelligent robot object detection techniques. In this process, the relationship between the center position of the target in the frame used for object detection and tracking is analyzed. It uses Laplace probability density as the parameter to calculate the center position relationship, and joins the correction network to correct the feature point positioning. The object re-capture function is added to solve the problem of object loss in the long-term object detection task, and the classifier is used to realize the object recognition. The results show that when the number of targets in the image reaches 20, the capture accuracy of the two data sets remains above 98.7%. In the intersection to union ratio test, when the real rectangular box contained 2M pixels, the intersection to union ratio of the method proposed in this study remains at or above 0.989. When conducting actual application memory usage tests, the proposed method maintains a memory usage of less than 2000Mb at runtime. It is shown that this method has better target detection efficiency and quality, and the requirement of hardware is lower.

Abstract Moving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Abstract In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.

Abstract Although Dynamic Movement Primitives (DMP) is an effective tool for robotic arm trajectory generalisation, the application of DMP in the 3C (Computer, Communication, Consumer Electronics) industry still faces challenges such as low precision and high‐time consumption. To address this problem, we propose a novel Cauchy DMP framework. The main improvements and advantages of Cauchy DMP, compared to the original DMP, are (1) since the Cauchy distribution has a simpler model and wider shape, using the Cauchy distribution instead of the Gaussian distribution in the original DMP reduces the complexity of the algorithm and saves time. (2) Singular Value Decomposition (SVD) can effectively model the error. To reduce the interference of the rounding and human error on the trajectory, SVD can be used to obtain the weight of each basis function. The proposed Cauchy DMP framework combines the above two points and is validated on a real UR5 robotic arm. The results show that Cauchy DMP retains the learnability of the original DMP and has the advantages of short time consumption and low error rate.

Moving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method. [ABSTRACT FROM AUTHOR]

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In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions. [ABSTRACT FROM AUTHOR]

Although Dynamic Movement Primitives (DMP) is an effective tool for robotic arm trajectory generalisation, the application of DMP in the 3C (Computer, Communication, Consumer Electronics) industry still faces challenges such as low precision and high‐time consumption. To address this problem, we propose a novel Cauchy DMP framework. The main improvements and advantages of Cauchy DMP, compared to the original DMP, are (1) since the Cauchy distribution has a simpler model and wider shape, using the Cauchy distribution instead of the Gaussian distribution in the original DMP reduces the complexity of the algorithm and saves time. (2) Singular Value Decomposition (SVD) can effectively model the error. To reduce the interference of the rounding and human error on the trajectory, SVD can be used to obtain the weight of each basis function. The proposed Cauchy DMP framework combines the above two points and is validated on a real UR5 robotic arm. The results show that Cauchy DMP retains the learnability of the original DMP and has the advantages of short time consumption and low error rate. [ABSTRACT FROM AUTHOR]

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot's long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method's advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object. [ABSTRACT FROM AUTHOR]

In this text, I develop further the concept of ‘larval subjects’ that I have borrowed from Gilles Deleuze and used in my book Life with Dementia. Angles towards elusive attempts. ‘Larval subjects’ as pre-perceptive intuitions of the body as a whole, before the kinaestheses, coming before the work of senses that weakens and could deteriorate with age or as a result of health disorders. But even then, the ‘system of passive Me-s’ stays bare as a last fundament. It is in this quantum state, manifested e.g. by life with dementia, where, as I assume, sovereign forces are hidden that permit no computational simulation. In the same time, robots are more and more used as social assistants, companions governed by artificial intelligence, including the care for dementia patients. Here I seek, with no claims of being exhaustive, working answers to the following questions: Can intelligent robots fill in gaps in the everyday life of people with psychosomatic problems and more specifically of elderly people with dementia? What eff ects on the person with health disorders and the dynamics of the illness could have even the partial robotization of everyday care? What are those robots – enhancement tools (prostheses), simulations of pets, or artificial humans? [ABSTRACT FROM AUTHOR]

To make mobile robot move considerately, an adaptive motion control method integrated potential field method is proposed. Risk map is firstly defined to show the magnitude of risks in the empty spaces around objects, then the considerate path is generated based on based on risk map. The robot is controlled by using integrated potential field method. The robot can automatically move with a considerate path to avoid disturbing others without make multiple moving rules, and areas with medium level risk are still passable, when there is no better choice. Besides, the robot will still make the users feel conformable by using effective interactions, like moving slowly to make sure safety or warning the users by sound. By comparing the experimental results with those controlled by conventional potential field method, the proposed method can control the robot move more adaptively and considerately with appropriate interactions.

Guide mobile robot have been researched and developed for decades. However, current robot cannot guide the user to the destination considerately, since the status of the user and the properties of the obstacles are not considered, especially for the guiding work of servicing visually impaired people. In this paper, a guide mobile robot with an easy-to-hold handle is designed, a generation method of spatial risk map is proposed to evaluate the influences of potential spaces of objects and a motion control method based on spatial risk map considering potential occupied spaces of objects is proposed. The users are successfully guided to the destination naturally without influencing other pedestrians by avoiding entering the potential spaces of objects automatically, and they can adjust their moving status in their own will since the considerate robot can adaptively adjust its guide status to adapt to the user. Through comparing guiding experiments of different tasks for 10 blindfolded users, the proposed guide mobile robot is proven considerate by guiding the users adaptively and make other pedestrians feel comfortable by avoiding entering the potential spaces of objects automatically.

Human-robot teaming receives an ever-increasing level of attention in research, development and industry. Novel approaches to task sharing in hybrid teams range from optimized schedules to intelligent cobot assistants with a high degree of autonomy. These approaches must prove their usefulness and benefits compared to manual work or full automation – particularly when it comes to assessing their potential for productive industrial use. This leverages demand for standardized, repeatable benchmarks to compare approaches and measure improvements in a reproducible way. Designing such benchmarks is challenging as numerous aspects, from safety considerations to human factors and team performance, must be considered. This survey seeks to contribute to the future development of benchmarks for the field of collaborative assembly, handling, and industrial cobot applications by giving a comprehensive overview of relevant metrics, evaluation strategies, and tasks for human-robot teams.

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

This paper presents a method on how to compute the position of a robotic arm in the 2D and 3D spaces. This method is slightly different from the well-known methods, such as forward or inverse kinematics. The method presented in this paper is an optical method, which uses two video cameras in stereo vision configuration to locate and compute the next move of a robotic arm in space. The method recognizes the coordinates of the markers placed at the joints of the robotic arm using the two video cameras. The coordinate points of these markers are connected with straight lines. Around certain points, circles are drawn. From the tangent to the circles, a non-Cartesian (orthogonal) coordinate system is drawn, which is enough to compute the target position of the robotic arm. All of these drawings are overlaid on the live video feed. This paper also presents another method for calculating the stereo distance using the triangulation method. An alternative method is also presented when a non-Cartesian (orthogonal) 3D coordinate system is created, which is used to compute the target position of the robotic arm in the 3D space. Because the system is in a loop, it can make micro-adjustments of the robotic arm, in order to be exactly in the desired position. In this way, there is no need to make calibrations for the robotic arm. In an industrial system, there is no need to stop the production line, which can be a really big cost saver. [ABSTRACT FROM AUTHOR]

User‐interactive interfaces, converting tactile stimuli into readable signals to users and devices simultaneously, improve the communication and interaction between human and machines and therefore greatly contribute to the safety and dexterity during the interactions. However, the concomitant challenges of current user‐interactive interfaces such as complex architecture, massive electrodes and fussy cable connections, bulky and unhandy power supply, and deficiency in multistimuli responses have yet to be solved. Herein, an all‐optical tactile sensing platform consisting of heterogeneous mechanoluminescent materials and polymer matrix is proposed for the conversion of multiple tactile stimuli into heterochromatic lights in an untethered and self‐powered manner. The all‐optical tactile sensing platform can respond to tiny shear force such as fingertip slipping with a low limit of 2 N and wide range of strains ranging from 30% to 70% with appropriate discrimination. Most importantly, the visualization of tactile stimuli with human‐ and machine‐readability and vividness permit remote and wireless user‐interactive applications such as videogames and RC car control, assisted with the developed method of active optical signal recognition. This work presents a paradigm shift to user‐interactive interfaces, boosting their implementation in multitudinous areas such as artificial intelligences and the Internet of Things. [ABSTRACT FROM AUTHOR]

Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot. [ABSTRACT FROM AUTHOR]

The foot paddling of an underwater robot causes continuous changes of the water flow field, which results in the unbalanced hydrodynamic force to change the robot's posture continuously. As the water environment and robot swimming are nonlinear and strongly coupled systems, it is difficult to establish an accurate model. This paper presents an underwater robot, which adopts the synchronous and alternate swimming trajectory of a beaver. Its pitch stability control model is established by using deep reinforcement learning algorithm and its self-learning control system is constructed for stable control of pitch attitude. Experiments are conducted to show that the pitch attitude of the beaver-like underwater robot can be stabilized while maintaining a certain swimming speed. The control method does not need to establish a complex and high-order model of webbed paddling hydrodynamics, which provides a new idea for stable swimming control of underwater robots. This work aims to find an excellent control method for underwater bionic robots. The ocean has the richest natural resources and the most diverse species on Earth. The underwater environment is complex and variable, imposing higher demands on the performance of underwater robots. Increasingly, new concept marine equipment is being researched for scientific exploration, and among these, underwater robots designed based on bionic principles are a growing trend. Currently, most underwater robots still use propellers as their propulsion system. Propellers have advantages such as simple control, high mechanical efficiency, and powerful propulsion, but they also have drawbacks including severe water flow disturbance during operation, high noise, poor concealment, and limited adaptability in complex water environments. Finding a propulsion system with better overall performance is a crucial way to enhance the motion capabilities of underwater robots. Underwater robots often have complex structures, and there are numerous factors influencing their movement in the underwater environment, making fluid dynamics modeling and optimization challenging. Reinforcement learning, as an optimization algorithm, can circumvent the aforementioned difficulties. • An underwater robot adopting the synchronous and alternate swimming trajectory of a beaver is presented. • The pitch stability control model is established using DRL and the self-learning control system is constructed for stable control of pitch attitude. • Experiments are conducted to show the pitch attitude of the beaver-like robot can be stabilized while maintaining a certain swimming speed. [ABSTRACT FROM AUTHOR]

Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords ‘robotics’ and several NNDs such as ‘ADHD’, ’Autism Spectrum Disorder (ASD)’, ‘cerebral palsy’, and ‘dementia’ in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

When the infectious coronavirus SARS-CoV-2 broke out it resulted in a global crisis. In the fight of Covid-19, China’s government relied on its strength to apply new technologies, i.e. for controlling and containment of the virus by tracing and tracking Chinese citizens. Relying on the trajectory of industrialisation, China has pursued a path of innovation. While it is reasoned that China’s advantage might have origin in the experience of the SARS outbreak almost two decades ago, this article argues that mainly China’s innovation- driven climate has favoured the application of new technologies in combatting the current crisis. Based on the innovation-driven trajectory this article explores China’s pathway out the corona crisis and how this might strengthen China’s role in global health governance. In order to pursue this aim, this article explores several areas, in which the next generation of technologies, such as AI-based diagnostic or intelligent robots were applied and concludes with an outlook based on the formulated political agenda, strategic considerations and initial international cooperation regarding China’s impact for global health.

In China, rural children, compared to their urban counterparts, are disadvantaged by less parental care and limited access to educational resources. This can have dire consequences for their health-related well-being during a vulnerable and formative stage of the life course. Quality of life (QOL) has been increasingly used as an indicator of individuals' well-being. QOL studies have primarily been conducted in Western contexts, but there is relatively less knowledge on QOL in the Chinese context, particularly for rural left-behind children (LBC). This study analyzed the effects of a socially innovative product – intelligent robots by the Li Ensan Charity Foundation – on the health-related well-being of rural LBC in China. Children who used the robots and reported higher satisfaction with the use experienced positive effects on QOL than their counterparts.. We discussed the implications of these results in the context of micro- and macro-level practice. [ABSTRACT FROM AUTHOR]

Background: The twenty first- century marked the exponential growth in the use of intelligent robots and artificial intelligent in nursing compared to the previous decades. To the best of our knowledge, this article is first in responding to question, "Can nurses in clinical practice ascribe responsibility to intelligent robots and artificial intelligence when they commit errors?". Purpose: The objective of this article is to present two worldviews (anthropocentrism and biocentrism) in responding to the question at hand chosen based on the roles of the entities involved in the use of intelligent robots and artificial intelligence in nursing. Methods: The development of this article was motivated by the immense discoveries, the current landscape, and nurses' role in relation to advanced technologies in healthcare. The paper begins the discussion by situating the use of intelligent robots and artificial intelligence in nursing and healthcare and presenting its ethical and moral implications. Then, we presented the two worldviews: anthropocentrism and biocentrism which are used to respond to the task at hand. Results: Anthropocentrism puts humans in the center in terms of moral standing and thus responsibility rests on them alone. Biocentrism declares that all creations deserve moral consideration and thus responsibility is equally allocated to all entities. Within these two worldviews, consensus development was offered to resolve these issues. Consensus provides clarity and democracy between and among the societies. Conclusions: The findings of this article can be basis in (1) instituting mechanisms of robust peer review and a rigorous series of simulation before adopting or implementing intelligent robots and artificial intelligence in clinical practice; (2) education and training of highly specialized nurse practitioners who can be focal persons in responding to ethical and moral issues with regard to these advanced technologies; and (3) harmonization of robotics research, manufacturing, and clinical practice. [ABSTRACT FROM AUTHOR]

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method. [ABSTRACT FROM AUTHOR]

With the increasing adoption of robotics in professional applications, the question arises of what impact robots with more cognitive skills will have on the labor market. Since such intelligent robots can highly affect the way of doing business, prior studies have mainly targeted their economic impact on work productivity. This article, on the other hand, focuses on the acceptance of intelligent robots by employees. We conducted 48 semi-structured interviews with office workers and managers. Based on three dilemmas, this paper uncovers why such employees would leave their work practices (fully or partially) to intelligent robots. Our findings show that many tasks can already be replaced with the necessary support. Employees seem highly positive about robots in the workplace and feel comfortable leaving simple tasks. Since they are especially skeptical about using robots for social, creative or confident tasks, proper guidance and training are crucial. By looking at the human level of intelligent robots, we add social and ethical considerations. Organizations gain insight into how employees typically view robotic changes to proactively react to employee concerns by gradually adopting their corporate innovation strategy. This study also provides an impetus for further research, with the ultimate aim of humanizing digital work. [ABSTRACT FROM AUTHOR]