In minimally invasive robotic surgery, the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a remote center of motion (RCM). Serial-link manipulators can be used as macro robots on which microsurgical robotic instruments are mounted to increase the number of degrees of freedom of the system and ensure safe task and RCM motion execution. However, the surgical instrument needs to be placed in an appropriate configuration when completing the motion tasks. The contribution of this article is to present a novel framework that preoperatively identifies the best base configuration, in terms of Roll, Pitch, and Yaw angles, of the microsurgical instrument with respect to the macro serial-link manipulator’s end effector in order to achieve the maximum accuracy and dexterity in performing specified tasks. The framework relies on hierarchical quadratic programming for the control, genetic algorithm for the optimization, and on a resilience to error strategy to make sure deviations from the optimum do not affect the system’s performance. Simulation results show that the mounting configuration of the surgical instrument significantly impacts the performance of the whole macro–micro manipulator in executing the desired motion tasks, and both the simulation and experimental results demonstrate that the proposed optimization method improves the overall performance.

Effective teleoperation of the small-scale and highly-integrated robots for single-port surgery (SPS) imposes unique control and human-robot interaction challenges. Traditional isometric teleoperation schemes mainly focus on end-to-end trajectory mapping, which is problematic when applied to SPS robotic control, especially for dual-arm coordinated operation. Inspired by the human arm configuration in boxing maneuvers, an optimized anthropomorphic coordinated control strategy based on a dual-step optimization approach is proposed. Theoretical derivation and solvability of the problem are addressed, and the effectiveness of the method is further demonstrated in detailed simulation and in-vitro experiments. The proposed control strategy has been shown to perform dexterous SPS bimanual manipulation more effectively, involving less instrument-interference and is free from singularities, thereby improving the safety and efficiency of SPS operations.

The upper boundary of time delay is often required in traditional telesurgery control design, which would result in infeasibility of telesurgery across regions. To overcome this issue, this paper introduces a new control framework based on deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm. The developed framework effectively overcomes the phase difference and data loss caused by time delays, which facilitates the restoration of surgeon’s intention and interactive force. Kalman filter (KF) is employed to blend multiple surgeons’ commands and predict the final local commands, respectively. The control framework ensures synchronization tracking performance and transparency. Prior knowledge of time delay is therefore not required. Simulation and experiment results have demonstrated the merits of the proposed framework.

Tendon-driven flexible surgical robots are normally suffering from the inaccurate modeling and imprecise motion control problems due to the nonlinearities of tendon transmission. Learning-based approaches are experimental data-driven with uncertainties modeled empirically, which can be adopted to improve the inevitable issues. This work proposes a LSTM-based kinematic modeling approach with task-based data for a flexible tendon-driven surgical robot to improve the control accuracy. Real experiments demonstrated the effectiveness and superiority of the proposed learned model when completing path following tasks, especially compared to the traditional modeling.

Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analytically. In this work we analyse how incorporating a backlash model and compensation can improve model learning and control. We combine a backlash compensation technique and a Feedforward Artificial Neural Network (ANN) with differential relationships to learn the kinematics at position and velocity level of highly articulated tendon-driven robots. Experimental results show that the proposed backlash compensation is effective in reducing nonlinearities in the system, that compensating for backlash improves model learning and control, and that our proposed ANN outperforms traditional ANN in terms of path tracking accuracy.

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution. Note to Practitioners —The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.