The continuous advancement of minimally invasive surgical robotics has revealed significant limitations in conventional virtual simulation platforms, particularly in simulating dexterous manipulator movements and real-time interactive capabilities. These shortcomings substantially hinder effective surgical training and medical education. This study addresses these critical gaps by developing an innovative virtual simulation system that integrates computational precision with realistic visualization. The primary objectives are to create a miniaturized yet highly dexterous surgical manipulator, establish accurate kinematic models, and implement an intuitive master–slave control scheme to bridge the gap between theoretical training and practical surgical applications.

The research integrates multiple technical disciplines through a comprehensive co-simulation platform combining Simulink’s computational advantages with V-REP’s visualization capabilities. The mechanical design phase resulted in a novel 3-PRS-like flexible parallel manipulator architecture, achieving six-degree-of-freedom (DOF) motion within a 5 mm diameter. Key techniques include replacing conventional rigid spherical joints with “steel tube–flexible cable–steel tube” hybrid structures, implementing Bowden cable transmission for prismatic joints, and employing a unique two-stage serial-parallel configuration that significantly enhances workspace coverage while maintaining precision. The inverse kinematics solution utilized Newton–Raphson iteration with numerical Jacobian computation, achieving real-time performance through Simulink implementation. For control system development, a master–slave architecture was implemented using the 3D Systems TOUCH™ haptic device as the master controller. This involved coordinate transformation algorithms, motion scaling with saturation limits, and remote API communication protocols to ensure seamless interaction between physical input and virtual manipulator response. The virtual environment focused on clinically relevant training scenarios, requiring precise integration of anatomical models, sensor systems including vision and proximity detection modules, and realistic tissue interaction physics. In this study, the throat was selected as the simulation scene for sampling experiments. System validation encompassed workspace analysis, trajectory tracking accuracy tests, and qualitative assessment by surgical trainees.

Experimental validation demonstrated system performance across multiple metrics. The manipulator achieves precise six-DOF control with a workspace covering 9 mm radial range and 60 mm height. The master–slave control system maintains latency below 50 ms through optimized communication protocols. In practical training scenarios, the system successfully simulated complex laryngeal procedures with realistic tool–tissue interaction. Integrated sensor systems provided comprehensive visual feedback and collision avoidance, while the haptic interface delivered intuitive control matching natural surgical gestures.

This study successfully develops a virtual simulation system that overcomes traditional platform limitations through innovative mechanism design and hybrid simulation architecture. The flexible parallel manipulator provides a new solution for miniaturized surgical instruments, reconciling the competing demands of high dexterity, small size, and precise control. The hybrid Simulink–V-REP platform demonstrates that computational modeling and three-dimensional visualization can be synergistically combined to create realistic training environments. From an educational perspective, the system offers advantages over traditional risk-free exploration of surgical techniques. The successful implementation of master–slave control with haptic feedback bridges the gap between virtual simulation and actual surgical console operation, potentially accelerating the learning curve for robotic surgery. Future development directions include expanding the surgical scenario library, integrating highly sophisticated tissue deformation models, and incorporating machine learning techniques for adaptive training progression.

The South-to-North water diversion project is a strategic project in China. Since its construction, it has become the main source of water conservancy in more than 280 cities.The diversion tunnel is the key building to support the South-to-North water diversion project. Due to its long line, large diameter, high water pressure, complex surrounding rock geology, as well as many years of water conservancy erosion, biochemical substances erosion, geological effect and other influences, typical defects such as cracks, collapse, exposed steel bars are prone to occur. Artificial detection of defects in the tunnel not only consumes time and energy, but also has low accuracy and timeliness. Therefore, underwater robot inspection technology has become a hotspot of current research.Among them, the underwater manipulator can not only be installed on the underwater vehicle, but also can be selectively installed on the required platform to complete the tasks of cleaning the water surface, laying and repairing cables, salvaging sunken objects, cutting off ropes and so on. However, the control of the underwater manipulator is more complicated and difficult due to its time-varying mechanics, nonlinear properties, external interference and hydrodynamic influence. The main purpose of this paper is to establish the dynamics model of the underwater manipulator and improve the accuracy of the trajectory tracking of the manipulator.

In this paper, a modeling method combining Newton-Euler equation and Morrison's dynamic model is proposed, and then the dynamic parameters are identified. Then, in order to improve the precise control ability of the manipulator in complex transient underwater environment, an adaptive sliding mode control method is designed based on compensating nonlinear dynamics model and using radial basis function (RBF) neural network to compensate the unmodeled and modeling errors of the system. Through the dynamic modeling in Section 4, a detailed dynamic simulation environment of the underwater manipulator is obtained. Gaussian noise errors with amplitudes of 5, 20, 15, 10, 8, and 5 N·m are set for each joint. On this basis, Experiment 1(P1): double loop proportional integral differential (PID) controller is designed for control simulation. Then, in experiment 2(P2), RBF neural network is used to make fitting compensation for system modeling errors and unmodeled items. In experiment 3(P3), dynamic model compensation is added on the basis of P2.

The trajectory tracking effect ratio of P2 and P3 was obviously better than that of P1 experiment, and the tracking effect of P3 experiment was also better than that of P2 experiment after compensating the dynamic model.

Through simulation, this paper has proved the effectiveness of the proposed hydrodynamic modeling of the manipulator, and on the basis of compensating nonlinear dynamic model, The adaptive sliding mode control method using RBF neural network to compensate the unmodeled and modeling errors of the system has higher trajectory tracking accuracy than the traditional PID control and the general RBF network adaptive sliding mode control.