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.
Highlights
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
A fiber Bragg grating (FBG) is susceptible to temperature, which needs to be considered in the static model
We proposed a learning-based tip contact force estimation method for tendon-driven continuum manipulators, which can be more general and efficient for tendon-driven Continuum manipulators (CMs) compared to the method of building a complex static model
Summary
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. Force-sensing technology is one of the major limitations in the development of surgical robots, because in master-slave surgical robotic systems that lack force feedback, the operator can rely on only visual feedback, such as the deformation of tissue under load to estimate the contact force[4] This approach is highly subjective and influenced by the experience of the surgeon, which poses an added risk for robot-assisted surgery. A wavelength shift in the FBG will be affected in the case of small radius bending[13], which will undoubtedly limit the maximum bending curvature of the CMs. With the development of deep learning techniques, there are researchers that use machine vision approaches to achieve force sensing for CMs. Su et al.[14] and Haouchine et al.[15] both present methods for visionbased force estimation in surgical robotic systems by detecting tissue deformation. These machine vision approaches require more computational resources and have a lower execution rate without GPU acceleration
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