A Force Sensing-based Pneumatics for Robotic Surgery using Neural Network

Takeishi, Hisami; Baldovino, Renann G.; Bugtai, Nilo T.; Dadios, Elmer P.

doi:10.1109/hnicem.2018.8666235

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…Data from Experiment 2 was processed using a multioutput regression deep neural network. Neural networks have started to be used for force sensing when the relationships between inputs are too complex for traditional mathematical algorithms [27]- [29]. All data was standardized and shuffled before being processed, and the network used an Adam optimization algorithm.…”

Section: Neural Network Architecturementioning

confidence: 99%

Soft, Stiffness-Controllable Sensing Tip for On-Demand Force Range Adjustment With Angled Force Direction Identification

et al. 2022

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Force sensors are essential for measuring and controlling robot-object interactions. However, current force sensors have limited usability in applications such as grasping and palpation, where the range of angled forces changes between tasks. To address this limitation this paper proposes a novel optical-based soft-tipped force sensor capable of adjusting its range and sensitivity through pneumatic modulation. This research describes the sensor's design and examines the relationship between the internal pressure of the sensor and its sensing range, sensitivity, single-axis force-sensing accuracy, and capability of measuring the angle and magnitude of non-normal forces. Results indicate that by increasing the pressure in the sensor, the sensing range can be increased and the sensitivity decreased. These results demonstrate that the sensor can measure normal forces reliably at each pressure using 4th order fits with root-mean-square error (RMSE)∈ [0.032 N 0.110 N]. Finally, it is also demonstrated that by using a neural network, the sensor can measure the angle and magnitude of non-normal forces with RMSEs on trained variables of 0.0120 Rad for Y-angle (θ Y ) measurements, 0.0109 Rad for X-angle (θ X ) measurements, and 0.102 N for force measurements.

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Section: Neural Network Architecturementioning

confidence: 99%

Soft, Stiffness-Controllable Sensing Tip for On-Demand Force Range Adjustment With Angled Force Direction Identification

et al. 2022

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“…In the field of construction engineering, artificial neural networks are used to predict concrete strength and find the nonlinear input-output relationship between concrete strength and its influencing factors [9,10]. In addition, artificial neural networks are used in the field of plant diseases control [11][12][13], process control and optimization [14][15][16], troubleshooting [17][18][19], intelligent control of industrial product assembly line [20][21][22], robotic surgery [23][24][25], intelligent driving [26][27][28], chemical product development [29][30][31], signal processing [32][33][34], and so on.…”

Section: The Origin and Development Of Artificial Neural Networkmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

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Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

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“…The application of artificial neural networks has gradually begun to shine in all walks of life. The main application areas are signal processing [14][15][16], plant diseases and insect pests and irrigation control [17,18], intelligent control of industrial product assembly line [19,20], intelligent driving [21,22], chemical product development [23][24][25], image processing [26][27][28], robotic surgery [29][30][31], automatic control of power systems [32][33][34], troubleshooting [35,36], process control and optimization [37][38][39], etc.…”

Section: The Development History Of Artificial Neural Networkmentioning

confidence: 99%

Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

Liang

Zhang

2021

AJRCoS

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Artificial Neural Network (ANN) is established by imitating the human brain's nerve thinking mode. Because of its strong nonlinear mapping ability, fault tolerance and self-learning ability, it is widely used in many fields such as intelligent driving, signal processing, process control and so on. This article introduces the basic principles, development history and three common neural network types of artificial neural networks, BP neural network, RBF neural network and convolutional neural network, focusing on the research progress of the practical application of neural networks in chemical process optimization.

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A Force Sensing-based Pneumatics for Robotic Surgery using Neural Network

Cited by 11 publications

References 15 publications

Soft, Stiffness-Controllable Sensing Tip for On-Demand Force Range Adjustment With Angled Force Direction Identification

Soft, Stiffness-Controllable Sensing Tip for On-Demand Force Range Adjustment With Angled Force Direction Identification

Optimization of Extraction Process Based on Neural Network

Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

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