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. 2021 Sep 1;11(1):17482.
doi: 10.1038/s41598-021-97003-1.

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

Fan Feng  1 , Wuzhou Hong  1 , Le Xie  2   3
Affiliations

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

Fan Feng et al. Sci Rep. .

Abstract

Although 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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Tendon-driven helical continuum manipulator subjected to a unidirectional contact force Fy.
Figure 2
Figure 2
(a) Three loading paths of a tendon-driven CM. (b) Path of tension changes influenced by interface friction.
Figure 3
Figure 3
Design of the real-time contact force estimator.
Figure 4
Figure 4
Training data acquisition platform.
Figure 5
Figure 5
Overview of the RNN-based contact force estimator.
Figure 6
Figure 6
Accuracy of the ANN-based estimators for Fx, Fy and |Fc|.
Figure 7
Figure 7
Dynamic performances of the real-time force estimators.
Figure 8
Figure 8
Comparison of the errors of each force estimator.
Figure 9
Figure 9
Manual decoupling with sub-estimators for two directions.
Figure 10
Figure 10
Performance comparisons between the two schemes.
See this image and copyright information in PMC

References

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