Robot peg-in-hole assembly based on contact force estimation compensated by convolutional neural network

A learning-based tip contact force estimation method for tendon-driven continuum manipulator

Scientific Reports ◽

10.1038/s41598-021-97003-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fan Feng ◽

Wuzhou Hong ◽

Le Xie

Keyword(s):

Neural Network ◽

Contact Force ◽

Recurrent Neural Network ◽

Estimation Method ◽

Training Data ◽

Force Sensing ◽

Force Estimation ◽

Advantages And Disadvantages ◽

Continuum Manipulator ◽

Three Degree Of Freedom

AbstractAlthough tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

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Sensorless Real-time Force Estimation in Microsurgery Robots using a Time Series Convolutional Neural Network

IEEE Access ◽

10.1109/access.2021.3124304 ◽

2021 ◽

pp. 1-1

Author(s):

Jiuyun Xia ◽

Kazuo Kiguchi

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Real Time ◽

Force Estimation

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A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2019.01.011 ◽

2019 ◽

Vol 50 ◽

pp. 134-150 ◽

Cited By ~ 12

Author(s):

Arturo Marban ◽

Vignesh Srinivasan ◽

Wojciech Samek ◽

Josep Fernández ◽

Alicia Casals

Keyword(s):

Neural Network ◽

Robotic Surgery ◽

Convolutional Neural Network ◽

Network Approach ◽

Force Estimation ◽

Neural Network Approach

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Artificial Intelligence-Based Optimal Grasping Control

Sensors ◽

10.3390/s20216390 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6390 ◽

Cited By ~ 1

Author(s):

Dongeon Kim ◽

Jonghak Lee ◽

Wan-Young Chung ◽

Jangmyung Lee

Keyword(s):

Neural Network ◽

Contact Force ◽

Arrival Time ◽

Contact Point ◽

Pressure Sensors ◽

Air Pressure ◽

Sensor Data ◽

Robot Hand ◽

Force Estimation ◽

Grasping Force

A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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