Search Results (17)

Teleoperation enables robots to perform tasks in dangerous or hard-to-reach environments on behalf of humans, but most methods lack operator immersion and compliance during grasping. To significantly enhance the operator’s sense of immersion and achieve more compliant and adaptive grasping of objects, we introduce a novel teleoperation method for dexterous robotic hands. This method integrates finger-to-finger force and vibrotactile feedback based on the Fuzzy Logic-Dynamic Compliant Primitives (FL-DCP) controller. It employs fuzzy logic theory to identify the stiffness of the object being grasped, facilitating more effective manipulation during teleoperated tasks. Utilizing Dynamic Compliant Primitives, the robotic hand implements adaptive impedance control in torque mode based on stiffness identification. Then the immersive bilateral teleoperation system integrates finger-to-finger force and vibrotactile feedback, with real-time force information from the robotic hand continuously transmitted back to the operator to enhance situational awareness and operational judgment. This bidirectional feedback loop increases the success rate of teleoperation and reduces operator fatigue, improving overall performance. Experimental results show that this bio-inspired method outperforms existing approaches in compliance and adaptability during teleoperation grasping tasks. This method mirrors how human naturally modulate muscle stiffness when interacting with different objects, integrating human-like decision-making and precise robotic control to advance teleoperated systems and pave the way for broader applications in remote environments. Full article

Grasping objects, from simple tasks to complex fine motor skills, is a key component of our daily activities. Our approach to facilitate the development of advanced prosthetics, robotic hands and human–machine interaction systems consists of collecting and combining surface electromyography (EMG) signals and contextual data of individuals performing manipulation tasks. In this context, the identification of patterns and prediction of hand grasp types is crucial, with cylindrical grasp being one of the most common and functional. Traditional approaches to grasp prediction often rely on unimodal data sources, limiting their ability to capture the complexity of real-world scenarios. In this work, grasp prediction models that integrate both EMG signals and contextual (task- and product-related) information have been explored to improve the prediction of cylindrical grasps during reaching movements. Three model architectures are presented: an EMG processing model based on convolutions that analyzes forearm surface EMG data, a fully connected model for processing contextual information, and a hybrid architecture combining both inputs resulting in a multimodal model. The results show that context has great predictive power. Variables such as object size and weight (product-related) were found to have a greater impact on model performance than task height (task-related). Combining EMG and product context yielded better results than using each data mode separately, confirming the importance of product context in improving EMG-based models of grasping. Full article

During the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator’s end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. Firstly, a sliding mode controller is designed for position control to mitigate uncertainties, including friction and unknown perturbations within the manipulator system. Secondly, the RBFNN, known for its nonlinear fitting capabilities, is employed to identify the system throughout the iterative process. Lastly, a gradient descent method adjusts the impedance parameters iteratively. Through simulation and experimentation, the efficacy of the proposed method in achieving precise force and position control is confirmed. Compared to traditional impedance control, manual adjustment of impedance parameters is unnecessary, and the method can adapt to tasks involving objects of varying stiffness, highlighting its superiority. Full article

This paper proposes an underactuated dexterous hand with novel bidirectional self-locking joints (BSJs) that enable multiple fingertip motion trajectories. The BSJ design integrates a locking wheel, rack, finger side walls, and a self-holding electromagnetic actuator, combining rack-and-pinion transmission with friction self-locking principles. Building on the BSJ concept, an underactuated dexterous hand is developed. The study begins with an analysis of BSJ’s deviation angle, establishing the minimum deviation angle critical to its operation. A detailed mechanical model of a BSJ is formulated, and its parameters are quantitatively analyzed to determine a safety static friction coefficient (0.177). Five distinct finger motion modes are designed and kinematic analysis focuses on the index finger and the generation of 57 unique fingertip active motion trajectories. Experimental validation included single finger performance tests that confirmed the diversity of fingertip trajectories and the hand’s ability to withstand loading in both forward and reverse directions. Through envelope and precision grasping experiments, the dexterous hand demonstrated its adaptability and ability to grasp objects of various sizes and shapes, such as strawberries, apples, student ID cards, and water bottles. This capability underscores its potential for a wide range of applications, from prosthetic hands for rehabilitation, where precision and adaptability are key, to robotic hands in industrial automation, offering flexibility in diverse tasks. Full article

Grasping the interrelationship between the supply and demand of ecosystem services (ESs) and spatial scale characteristics is the foundation for effective ecological zoning management, which helps to realize a win–win situation for both ecological protection and economic development. This paper focuses on the following three real problems: mismatch in ES supply and demand evaluation, mechanical and subjective delineation of ecological zoning, and rough management strategies, and constructs a multi-scale ecological zoning management framework for the “comprehensive evaluation of supply and demand, ecological zoning, and enhancement of human well-being”. This study integrates the InVEST model, SOM, Z-score quadrant matching, and coordination degree method, and applies them to the ecological management zoning of Hangzhou. The results show that (1) the spatial differentiation of ESs in Hangzhou is significant. The spatial pattern of the five types of ES supply varies at the county scale and the grid scale on which ES demand is concentrated and is consistent at different scales. (2) ES supply–demand matching in Hangzhou is at the basic coordination and can be divided into four modes including HH, LH, LL, and HL at both the county and grid scales. On the small scale, the proportion of mismatches declines slightly, but the severity rises. (3) ES supply is divided into four categories as follows: the food production bundle, the carbon storage bundle, the ESs balancing bundle, and the ESs depleting bundle, and clarifies the priority of ES management. (4) Construct an ecological management practice path, delineates 6 ecological management zones at the county scale and 19 secondary management zones at the grid scale. Targeted measures are proposed in terms of supply–demand adjustment strategies, ecological management strategies, and key implementation areas. This study helps to incorporate the interaction between the supply and demand of ESs into the planning framework and provides decision-making support for refined ecological management. Full article

The increasing demand for the adept handling of a diverse range of objects in various grasp scenarios has spurred the development of more efficient and adaptable robotic claws. This study specifically focuses on the creation of an adaptive magnetorheological fluid (MRF)-based robotic claw, driven by electro-permanent magnet (EPM) arrays to enhance gripping capabilities across different task requirements. In pursuit of this goal, a two-finger MRF-based robotic claw was introduced, featuring two magnetorheological (MR) grippers equipped with MR elastomer (MRE) bladders and EPM arrays at the fingertips. The operational principle involved placing a target object between these MR grippers and adjusting the normal force applied to the object for effective grasping. During this process, the contact stiffness of the MR grippers was altered by activating the EPM arrays in three distinct operation modes: passive, short-range (SR), and long-range (LR). Through experimentation on a benchtop material testing machine, the holding performance of the MRF-based robotic claw with the integrated EPM arrays was systematically evaluated. This study empirically validates the feasibility and effectiveness of the MRF-based robotic claw when equipped with EPM arrays. Full article

Risk assessments and risk prioritizations are crucial aspects of new product design before a product is launched into the market. Risk-ranking issues involve the information that is considered for the evaluation and objective weighting considerations of the evaluation factors that are presented by the data. However, typical risk-ranking methods cannot effectively grasp a comprehensive evaluation of this information and ignore the objective weight considerations of the risk factors, leading to inappropriate evaluation results. For a more accurate ranking result of the failure mode risk, this study proposes a novel, flexible risk-ranking approach that integrates spherical fuzzy sets and the objective weight considerations of the risk factors to process the risk-ranking issues. In the numerical case validation, a new product design risk assessment of electronic equipment was used as a numerically validated case, and the simulation results were compared with the risk priority number (RPN) method, improved risk priority number (IRPN) method, intuitionistic fuzzy weighted average (IFWA) method, and spherical weighted arithmetic average (SWAA) method. The test outcomes that were confirmed showed that the proposed novel, flexible risk-ranking approach could effectively grasp the comprehensive evaluation information and provide a more accurate ranking of the failure mode risk. Full article

Machine vision technology was integrated into the manufacturing workshop, to achieve an effective and high-quality production mode for furniture manufacturing. Machine vision can be used for information collecting, quality detecting, positioning, automatic sorting, intelligent monitoring, etc., which largely make up for the shortcomings of poor quality, low precision, low efficiency, and high labor intensity of manual operation. In this study, the method of systematic literature review was applied, and 128 relevant literatures in the field of machine vision application in manufacturing were retrieved and screened from 2011 to 2022. Statistical analysis was carried out on the extracted application directions and related technologies. The current status of machine vision technology’s implementation in furniture manufacturing was summarized. In view of the new demand of the rapid development of intelligent manufacturing, the challenges, faced by machine vision, were also summarized. To build a more intelligent, comprehensive, and effective manufacturing workshop for wooden products, cutting-edge technologies, such as deep learning and 3D point cloud, must be further integrated into machine vision. This study can efficiently assist the pertinent practitioners in furniture manufacturing in quickly grasping the pertinent technical principles and future development directions of machine vision, which would be benefit for accomplishing intelligent manufacturing. Full article

Building vertical greenery has become an effective measure to solve the contradiction between the shortage of urban greenery and the increasing demand for greenery. However, the lack of direct economic benefits dampens motivation for its development. As a vital development mode for high-density cities worldwide, commercial complexes are the most appropriate buildings to support greenery because of economic agglomeration and resource integration. An important reason for whether or not commercial complex greenery is constructed is the need for an evaluation system. To bridge this research gap, we propose a holistic evaluation guideline for commercial complex greenery. First, a list of related sustainable rating systems from the academic literature and official websites was compiled and reviewed to identify and compare their referential features. Second, the limitations of these evaluation systems in assessing the greenery of commercial complexes were explored using a case study. Third, the features of commercial complex greenery were introduced through field research and interviews. Finally, a holistic evaluation guideline for vertical greenery systems in commercial complexes was proposed, including logical thinking for the evaluation system of the dimension–indicator–quantitative method, the creation of innovative evaluation indicators, the establishment of a database, the assignment of weights to different dimensions and indicators, and the construction of an evaluation mechanism for the whole life cycle. This research demonstrates the significance of an evaluation process for commercial complex greenery systems, proposes a refined guideline for its development, and rationally grasps the development direction from a macro perspective. Full article

Communication robots have been introduced in nursing care, education, and the hospitality sector. In the future, robots will be increasingly integrated into human society, with more opportunities to interact closely with humans. Therefore, investigating the symbiosis between humans and robots is critical. Touch, including actions, such as shaking hands, holding hands, and touching shoulders are common in most societies. These actions are called the social touch and are common modes of communication. Social touch not only conveys emotions and intentions but also mental and physical effects. Touch considerably influences social relationships: for example, by creating positive impressions and enabling the fulfillment of requests. Since the development of communication robots and other robots capable of physical contact, touch communication between humans and robots has been extensively studied. Although studies have revealed that touching a robot positively influences the impression regarding the robot and induces a relaxed feeling, negative perceptions related to trust on the robot have been reported. Thus, touch interactions between humans and robots are yet to be fully understood. Studies have focused on the effects of touch, such as touching the robot or being touched by the robot. Although interactions with robots that respond to touch, such as hugging behavior, have been studied, few studies have examined the psychological effects of robot responses to other types of touch such as hitting, stroking, and grasping. In this study, a humanoid robot was used to investigate how the reactive behavior exhibited by the robot in response to touch by a participant affects the degree of favorability and intellectual impression toward the robot as well as the sense of accomplishment regarding communication. Participants exhibited high favorability, feeling of relief, and willingness to continue the interaction with robots that exhibited appropriate reactions to the touch of participants. Participants exhibited a positive impression when they decided the touch gesture of the robot rather than when instructed on how to touch it. The results of this study can provide guidelines for improving the design and utilization of robots, such as therapeutic robots, that work alongside humans. Full article

Giving robot hands more powerful functions has always been one of the goals pursued by scholars in this field. In this paper, an electromagnetic switching multiple grasping modes robot hand (ESMGM hand) is proposed, which integrates three typical grasping modes and therefore has versatile usage and improved performance. The switchable CPS mechanism developed in this paper integrated the parallel grasping and coupled grasping modes, which are incompatible with each other, through ingenious design. The partial effective transmission mechanism guarantees the fusion and connection to self-adaptive grasping mode from both parallel grasping mode and coupled grasping mode. Based on the above two essential mechanisms, the specific structure of the ESMGM robot hand is designed. Theoretical analyses for the kinematic and grasping forces are performed, and the results show that the ESMGM hand not only has multiple grasping functions but also has the characteristics of equitable grasping motions, adequate grasping forces, and stable grasping effects. To further verify the performance of the ESMGM hand, the prototype of the ESMGM hand is manufactured, and grasping experiments are performed. The grasping forces distribution results are consistent with the theoretical analysis results. The general grasping experiments also illustrate that the ESMGM hand has the features of fast electromagnetic switching speed, good adaptability, high stability, fast response, and broad application prospects. Full article

As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing. Full article

Explosive ordnance disposal (EOD) robots can replace humans that work in hazardous environments to ensure worker safety. Thus, they have been widely developed and deployed. However, existing EOD robots have some limitations in environmental adaptation, such as a single function, slow action speed, and limited vision. To overcome these shortcomings and solve the uncertain problem of bomb disposal on the firing range, we have developed an intelligent bomb disposal system that integrates autonomous unmanned aerial vehicle (UAV) navigation, deep learning, and other technologies. For the hardware structure of the system, we design an actuator constructed by a winch device and a mechanical gripper to grasp the unexploded ordnance (UXO), which is equipped under the six-rotor UAV. The integrated dual-vision Pan-Tilt-Zoom (PTZ) pod is applied in the system to monitor and photograph the deployment site for dropping live munitions. For the software structure of the system, the ground station exploits the YOLOv5 algorithm to detect the grenade targets for real-time video and accurately locate the landing point of the grenade. The operator remotely controls the UAV to grasp, transfer, and destroy grenades. Experiments on explosives defusal are performed, and the results show that our system is feasible with high recognition accuracy and strong maneuverability. Compared with the traditional mode of explosives defusal, the system can provide decision-makers with accurate information on the location of the grenade and at the same time better mitigate the potential casualties in the explosive demolition process. Full article

This article argues that it is not Buddhism, per se, but rather Buddhist extremism, that is responsible for violence against relevant out-groups. Moreover, it suggests that the causes of Buddhist extremism, rather than being determined solely by textual and scriptural justifications for out-group violence, are rooted instead in the intersection between social psychology and theology, rather than organically arising from the latter, per se. This article unpacks this argument by a deeper exploration of Theravada Buddhist extremism in Sri Lanka. It argues that religious extremism, including its Buddhist variant, is best understood as a fundamentalist belief system that justifies structural violence against relevant out-groups. A total of seven of the core characteristics of the religious extremist are identified and employed to better grasp how Buddhist extremism in Sri Lanka manifests itself on the ground. These are: the fixation with maintaining identity supremacy; in-group bias; out-group prejudice; emphasis on preserving in-group purity via avoidance of commingling with the out-group; low integrative complexity expressed in binary thinking; dangerous speech in both soft- and hard-modes; and finally, the quest for political power, by force if needed. Future research could, inter alia, explore how these seven characteristics also adequately describe other types of religious extremism. Full article

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design. Full article