An Accelerated Anti-Noise Adaptive Neural Network for Robotic Flexible Endoscope With Multitype Surgical Objectives and Constraints

In minimally invasive surgery (MIS), the field of view (FOV) control is crucial. Autonomous endoscope robots have been developed to facilitate MIS procedures by enabling autonomous surgical target tracking, thus reducing the workload on surgeons. However, existing visual servoing-based target tracking methods for autonomous endoscopes often overlook the insecurity stemming from restricted workspace conditions. Instances, such as collisions between the endoscope robot’s tip and the patient’s chest or abdominal wall pose risks to patient tissue, while extensive motion of the endoscope shaft may damage incision port tissue. Addressing these security concerns, this article proposes a novel approach called virtual fixture-based restricted workspace constraint (RWSC) to reconstruct the endoscope robot’s movement range. A quadratic programming (QP) optimization framework is employed to govern the robot’s motion, ensuring autonomous target tracking while adhering to RWSCs. To solve the QP problem, we propose an adaptive zeroing neural network (ZNN) featuring a newly designed activation function (AF). This AF enhances the ZNN with predefined-time convergence and noise rejection capabilities, making it especially suitable for time-sensitive and noise-prone surgical applications. Theoretical analysis and experimental results demonstrate that our adaptive ZNN achieves shorter convergence times than existing neural dynamic-based QP solvers. Physical validations show the efficacy of the proposed RWSCs in limiting the workspace of the endoscope robot, while the FOV control strategy enables autonomous target tracking of flexible endoscopes under diverse constraints and objectives.