In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.
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.
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.
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