For bilateral teleoperation, the haptic feedback demands the availability of accurate force information transmitted from the remote site. Nevertheless, due to the limitation of the size, the force sensor is usually attached outside of the patient's abdominal cavity for the surgical operation. Hence, it measures not only the interaction forces on the surgical tip but also the surgical tool dynamics. In this paper, a model-free based deep convolutional neural network (DCNN) structure is proposed for the tool dynamics identification, which features fast computation and noise robustness. After the tool dynamics identification using DCNN, the calibration is performed, and the bilateral teleoperation is demonstrated to verify the proposed method. The comparison results prove that the proposed DCNN model promises prominent performance than other methods. Low computational time (0.0031 seconds) is ensured by the rectified linear unit (ReLU) function, and the DCNN approach provides superior accuracy for predicting the noised dynamics force and enable its feasibility for bilateral teleoperation.

In this paper, a teleoperation control of a 7-DoF robot manipulator for Minimally Invasive Surgery (MIS), which guarantees a safety-enhanced compliant behavior in the null space, is described. The redundancy of the manipulator is exploited to provide a flexible workspace for nurses or other staff (assisting physicians, patient support). The issue with safety and accurate surgical task execution may arise in the presence of human-robot interaction. Based on the implemented impedance control of tele-operated MIS tasks, a safety enhanced constraint is applied on the compliant null space motion. At the same time, the control approach integrates an adaptive fuzzy compensator to guarantee the accuracy of the surgical tasks during the uncertain human-robot interaction. The performance of the proposed algorithm is verified with virtual surgical tasks. The results showed that the compliant null space motion is constrained in a safe area, and also that the accuracy of tool tip is improved, providing a flexible and safe collaborative behavior in the null space for human-robot interaction during surgical tasks.

In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitting (CF), the accuracy of the tool identification is improved. After the identification and calibration, a further demonstration of bilateral teleoperation is presented using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrate the promising performance of the model-free tool identification technique using MNN, improving the results provided by model-based methods.

In this paper, we provide a new generalized kinematic formulation of the Remote Center of Motion (RCM) constraint for serial robotic arms to be useful for implementation in torque-controlled robots. A minimally invasive surgery assisted by a serial robot could be used for inserting a surgical tool through a desired insertion position, i.e. trocar position. The restricted movement caused by the trocar, i.e. the RCM constraint, must be guaranteed by the robotic system in order to avoid injury to the patient. The generalized formulation, presented in this paper, associates the task-space with the RCM constraint coordinates, which are described by the minimum distance between the surgical tool and the trocar position. In order to simultaneously achieve the surgical tool-tip trajectory and the RCM constraint, two control methods are proposed and compared, one using a projection in the null-space, the other by combining both tasks using a task-space augmentation method. Moreover, when additional lower priority tasks have to be implemented in the null-space of the two higher priority tasks, the kinematic formulation simplifies the calculation of the null-space projectors. Furthermore, unlike the previous studies, the formulation represented can be applied not only in a kinematic control but also in a dynamic control approach. Simulations were conducted to validate effectiveness of the proposed formulation, using a planar 4-DOF system as well as the dynamic model of a Kuka LBR 7 iiwa R800 robot arm.