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![Examples of soft robotics and their applications; a Soft gripper capable of grasping to various objects (source: [22]). b A basic soft pneumatic gripper fabricated by lithography (source: [23]). c A soft robot that uses explosion as its locomotion (source: [13]). d A soft pneumatic wearable robot with applications in medical and rehabilitation robotics (source: [20])](publication/342532701/figure/fig1/AS:962179276734476@1606412824400/Examples-of-soft-robotics-and-their-applications-a-Soft-gripper-capable-of-grasping-to.jpg)
Examples of soft robotics and their applications; a Soft gripper capable of grasping to various objects (source: [22]). b A basic soft pneumatic gripper fabricated by lithography (source: [23]). c A soft robot that uses explosion as its locomotion (source: [13]). d A soft pneumatic wearable robot with applications in medical and rehabilitation robotics (source: [20])
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Soft robotics is a trending area of research that can revolutionize the use of robotics in industry 4.0 and cyber-physical systems including intelligent industrial systems and their interactions with the human. These robots have notable adaptability to objects and can facilitate many tasks in everyday life. One potential use of these robots is in m...
Citations
... Soft robotics 12-Universal soft pneumatic robotic gripper with variable effective length 13-Towards a soft pneumatic glove for hand rehabilitation 14-Modeling of soft fiber-reinforced bending actuators 15 [4] ، ربات توان های شانه زانو، بخشی [5] مچ ، [6] و غیره شده است. ربات دسته به تحریک عامل و نوع اساس بر را نرم های های تحریک مانند مختلفی، نیوماتیکی [7] الکتریکی ، [8] تاندونی ، [9] ،.. [10] پیچیده حرکات دارای برخی و ترکیبی و تر [11] وجود، این با هستند [20,21] . [16] . ...
Pneumatic Soft bending actuators as safe and highly adaptable robots are being considered by researchers, such that they become an ideal choice for making devices that interact more with humans. These advantages come along with several disadvantages. Their great adaptability is provided by their high degrees of freedom, and consequently, it makes the modeling a significant challenge for researchers. In this paper, the modeling of a soft fiber-reinforced bending actuator that is in contact with the environment has been conducted by utilizing the finite rigid element method. This method provides a context within which the modeling theories of traditional rigid robots, such as the Denavit-Hartenberg method, become usable for a soft continuum actuator. Therefore, in the following, resorting to this theory, the static and dynamic behavior of a soft actuator under the effect of the external load has been investigated and compared with the empirical results derived from a fabricated actuator. The results show a minimum accuracy of 9 percent, although being as simple as can be used for other purposes like implementing control systems based on that.
... In the aspect of control, the study of manipulator control for soft robots has achieved lots of attention. Due to the controllability of pressure control, the manipulator can accommodate different grasping working conditions [16][17][18]. Most researchers mainly studied the fluid hydraulic actuation [19][20][21][22][23][24] and pneumatic actuation [25][26][27][28] in the field of soft robot. ...
Mitigating fatigue damage and improving grasping performance are the two main challenging tasks of applying the soft manipulator into industrial production. In this paper, the grasping position optimization-based control strategy is proposed for the soft manipulator and the corresponding characteristics are studied theoretically and experimentally. Specifically, based on the simulation, the resultant stress of step-function-type channels at the same pressure condition that was smallest compared with those of sine-function- and ramp-function-type channels, hence, a pneumatic network with step-function-type channels was selected for the proposed soft manipulator. Furthermore, in order to improve the grasping performance, the kinematics, mechanical, and grasping modeling for the soft manipulator were established, and a control strategy considering the genetic algorithm is introduced to detect the optimal position of the soft manipulator. The corresponding fabrication process and experiments were conducted to cross verify the results of the modeling and the control strategy. It is demonstrated that the internal pressure of the soft manipulator was reduced by 13.05% at the optimal position, which effectively helped mitigate the fatigue damage of the soft manipulator and prolonged the lifespan.
... A fuzzy system can have several membership functions for each variable. These functions can take any shape, but these are more common with triangular, trapezoidal, or Gaussian shapes that require the use of a fuzzy controller to optimize fuzzy operations [30]. ...
This study aims to present a novel Self-regulating and Intelligence Meta-Heuristic-Fuzzy approach (As Methodological Contribution) for integrated and optimal Human Resource Allocation (HRA) in normal and critical conditions at SMEs (As Conceptual Contribution). In this research, a mathematical model of human resource allocation problem is presented, and then Sugeno Fuzzy Inference (SFI) model is used in the tasks rate adjustment layer. The SFI model is the main part of developing Gray Wolf Optimization (GWO) algorithm to reach the integrated and optimal allocation of available human resources under self-regulating attribute in the novel approach. The novel approach has tested and compared to the best researches using data previous researches and by the top five proposed methods in the researches (Includes: SGA, PRS, SRS, MIP, HM) based on three methods of evaluating the quality of solutions (GA-FSGS, MP-FSGS, GA-SGS). The results showed that increase of Ω from 15,000 to 25,000, and HM and SGA clearly performed better than other previous cases in the larger B100 and B200 datasets. Also, it is verified that the method had better results compare to all previous solving methods, and the quality of the solutions have been the best.
Fog computing provides an infrastructure for enhancing quality of services (QoS), especially for time-critical applications. The reach of the Internet of things (IoT) has extended to another dimensions, expanding from acquisition of data to device interconnections and to data-processing. This acceleration assimilate fog and cloud compuhting into a single system for improving QoS and resource utilization. Due to the heterogeneity of IoT devices, selecting suitable computation devices and allocating resources are substantial issues that need to be addressed for effective resource utilization. This work proposes a smart decision-making system for service placement based on the various parameters. The proposed work utilizes machine learning based techniques: clustering for the labelling of the services followed by neuro-fuzzy based ANFIS model for offloading the services. A 5-layered neuro-fuzzy inference model is implemented to represent as an intelligent decision-making system. This work provides a solution for the learning phase of ANFIS by employing a meta-heuristic-based algorithm. Three metaheuristic algorithms, i.e. GA-ANFIS, JAYA-ANFIS and PSO-ANFIS are implemented for the training of the ANFIS model. The effectiveness of the model has been examined for the prediction of computing layer for offloading of the services. The results are compared with each other as well as with the conventional gradient-based ANFIS model. Experiment shows that the evolutionary-based neuro-fuzzy models yield imperative results against gradient-based neuro-fuzzy.
Fog computing has emerged as one of the most important Internet infrastructures for improving service quality, particularly in real-time applications. Due to the convergence in technologies, the scope of the Internet of things (IoT) has evolved to a new dimension, it expands from data collection to device interconnections, and to pre-processing. This acceleration involves cloud and fog computing layers into the system which plays an integral role in IoT data storage and computing. Due to the diversity present in IoT devices, selection of computation devices and allocation of resources are major challenges to be addressed for efficient utilization of resources. In this paper, we presented the offloading and resource allocation model to address the solution to the above challenge. Firstly, a 5-layered neuro-fuzzy model is introduced to retrieve the fuzzy sets and rules which further passes to the fuzzy inference system to model an orchestration decision system. Additionally, to improve the system performance, we have presented the modified least loaded resource allocation algorithm which is adaptively required to reduce the failure rate of the applications. To showcase the efficacy of the model, 4 healthcare applications (augmented reality, patient pre-monitoring, record analysis, and billing systems) are evaluated with their heterogeneous parameters. The simulation findings show that our suggested model improves system performance by lowering network latency by 2.23–9.68 %, computation delay by 3.40–13.66 %, and system performance by 1.03–11.55%. The simulation results demonstrated the suggested model’s resilience in terms of network latency, computation time, and failure rate.
