It is well-known that the tendon-driven robotic manipulator plays an important role in robotic-assisted minimally invasive surgery (MIS). However, due to the intrinsic nonlinearities, uncertainties, slack and hysteresis introduced by the tendon-driven actuation, the tendon-driven robotic manipulator is difficult to model and control when compared with the traditional actuation styles. To serve the modeling purpose, in this paper, the deep-learning-based intelligent modeling of inverse kinematics in the snake-like tendon-driven surgical instrument is presented. In the proposed approach the Deep Recurrent Neural Network (DRNN) with Long Short-Term Memory (LSTM) architecture is adopted to memorize and identify the nonlinear inverse kinematics of the tendon-driven surgical instrument through the history of the motor and tip positions. To collect highly reliable data to train the DRNN, the experiment to generate training data is carefully designed with the consideration of the stainless tendon characters and motor limitations. During the designed controller movements, the kinematics data is obtained by recording the motor positions and the tip positions. Besides, it is noticed that there are correlations of the sequential data samples, which could significantly reduce the modeling accuracy. To remove the correlations and improve the modeling performance, the correlations of the sequential data samples are removed by modifying the training processes. Modeling results and detailed discussions verified the effectiveness of the proposed approach.

Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical sub tasks for the construction of the shared control mechanism. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMP) is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation.

Snake-like robots for endoscopic surgery make it possible to reach deep-seated lesions. With the use of small flexible tendon-driven instruments, it is possible to perform bimanual micro-surgical tasks that are challenging for standard endoscopic surgeries. Existing devices, however, lack articulated wrists and rolling motion of the end-effector. This paper presents a new instrument design with a distal-roll gripper for snake-like robots. The developed 5 DoFs miniaturized instruments with a diameter of 3 mm enable the deployment into narrow endoluminal channels. Issues related to actuation coupling, tendon slack, and backlash are addressed. Experimental results show that the distal-roll gripper can rotate 106°, and the actuated joints can achieve good repeatability and accuracy with the proposed compensation control scheme.

Accurately detecting tumors and estimating the depth of tumors is essential in the surgical removal of tumors. In robotic-assisted surgery, autonomous robotic palpation has the potential to provide more precise detection, tumors' depth estimation, and less intrusion when normal tissues surround tumors. In this article, by mimicking the human finger touch, we propose a tactile sensing-based deep recurrent neural network (DRNN) with long short-term memory (LSTM) architecture to improve the accuracy of the detection and depth estimation of tumors embedded in soft tissue. In the experimental setup, the hard inclusions simulate the tumors, while the phantom tissue is fabricated by silicon to simulate the soft tissue. During the experiment, the data from the force sensor and displacement of the robot palpation probe are for detection and depth estimation purposes. The collected sequential data set of the force and the displacement of the probe during one completed palpation process will go through the proposed DRNN network with deep LSTM architecture, in which the temporal dependencies of the sequential data will be captured in the cell states in the deep LSTM layers. Subsequently, the softmax classifier is adopted to determine if there is any hard inclusion exists and offer the depth estimation of the hard inclusions. Experiments based on 396 real data sets demonstrate that the detection accuracy for the testing data set is 99.2% and the depth estimation accuracy for the testing data set is 95.8%. The accuracy of the proposed method is best when comparing with other widely used methods. Note to Practitioners-The palpation of tumors motivated this article in the robot-assisted surgical systems through tactile feedback. In order to mimic the human touch on the soft tissue, this article presents a deep-learning-based approach to estimate the depth of the hard inclusions in the phantom tissue through force information. The displacement of the palpation probe and the touch force during one palpation are recorded as data sequences to train the deep model, which aims to capture dynamics and long-term dependence of the palpation process. In this article, we made the first successful attempt to accurately estimate the depth of the hard inclusions buried at different locations of the phantom tissue using only force information. The proposed approach can work in different robot-assisted scenarios, such as master-slave robotic surgery. In the clinic applications, the force sensor will be integrated at the end-effector of the robotic manipulator. According to the specific requirements, the force sensor and the robotic manipulator might be different from those used in this article. For some applications, such as the laparoscopic interventions, the complete vertical contact tends to be difficult to obtain due to the laparoscopic port effects. The projection of the recorded force data and displacement can obtain the information in the normal direction. The future work is going to be extended to tissue environments with arbitrary surface and tumors with various shapes/depths for more complex and prospective clinical applications.