In intraocular microsurgery with minute operational scales, instruments pass through non-visible regions of the anterior segment, where robot-assisted surgery, which heavily relies on visual perception, fails to determine the instrument’s attitude relative to the eyeball. This compromises surgical flexibility, increases risks, and hinders autonomous surgery development. Therefore, a framework for predicting instrument trajectories in non-visible regions during robot-assisted microsurgery has been proposed to mitigate the risks of retinal and lens injuries caused by blind operations and enhance surgical procedures’ intelligence and autonomy. First, a lightweight reconstruction of the anterior segment environment is performed under controlled knowledge guidance to construct a global map. Second, the tip position of the surgical instrument is detected through multi-sensor fusion, enabling the perception of instrument-environment interactions under visual constraints. Based on this, a long short-term spatiotemporal aggregation algorithm for instrument trajectory prediction is proposed, which enhances surgical safety by providing high-precision predictions of the instrument tip’s motion trajectory. Experiments show that the framework achieved a 0.0435 mm average prediction error in non-visible regions, corresponding to 0.03% of the region in a single dimension and 7.25% of the surgical instrument’s diameter. This significantly enhances the precision of robot-assisted surgery under visual constraints and provides robust technical support for safe, intelligent, and autonomous intraocular robotic surgery.

Creating an intelligent surgical environment requires not only advanced robotic systems but also optimized microscopic imaging. However, autofocus remains a fundamental challenge, with current methods suffering from slow iterative processes or directional ambiguity, which compromises real-time performance. This paper presents an implicit disparity-blur alignment approach for robotic microsurgical autofocus, integrating stereo geometry’s monotonic depth cues with de-focus characteristics for rapid convergence. A novel physics-guided dual-stream network is developed to encode implicit depth representations through hierarchical cross-pathway feature fusion, enabling reliable focus prediction without explicit stereo matching in blur-degraded regions. An ROI-aware attention module is proposed to dynamically optimize focus-critical regions, coupled with learnable physics-guided kernel learning for precise Z-offset estimation. The approach achieves a top directional accuracy of 94.85% and a single-pass focus error of 0.20 mm with an inference time of 53 ms on a surgical dataset, which outperforms state-of-the-art methods in reducing iteration count by 22.8% and inference time by 51.8%. An intelligent robotic microscope prototype is developed, with validation through ex vivo tests demonstrating its ability to enable fast and precise multi-region focusing for microsurgeries.

This research addresses the challenge of achieving high-precision tracking of time-varying trajectories under nonlinear disturbances and motion constraints in microsurgical robots. A hybrid control framework integrating fuzzy adaptive sliding mode control with radial basis function neural networks is proposed. This framework dynamically adjusts the sliding mode gain to suppress high-frequency jitter and compensate for unmodeled disturbances such as joint friction and tissue contact forces. Experiments conducted on a self-developed microscopic ophthalmic robot platform demonstrated that the trajectory tracking error was reduced to 1.1 μm, representing improvements of 85.9%, 76.1%, and 66.7% compared to PID control, sliding mode control and non-singular fast terminal sliding mode control respectively. The tracking delay was 19 milliseconds. In experiments on living pigs with central retinal artery occlusion, the system successfully performed intravascular injection, with a maximum error of 3.97 μm. This solution, through optimization via fuzzy logic and neural networks, achieves micron-level precision and robustness, effectively solving high-frequency control noise and low-frequency environmental disturbances, ensuring both the accuracy and safety of the microsurgical robot.

The transbronchial interventional surgery presents challenges with winding and convoluted pathways, prone to compression and friction. Current autonomous planning struggles to reach deeper bronchial positions, and hard to consider multiple conflicting goals simultaneously. This article introduces an innovative planning scheme with preference weights to achieve smooth, frictionless, and collision-free autonomous transbronchial intervention with continuum robot (CR). A few-human-interaction twin-delayed deep deterministic policy gradient (FHITD3) generated from surgeon preference guidance is proposed, which determines the optimal strategy for the motion of CR. Preference knowledge is generated through interaction between human and few diversity samples. An abstract actuator space description is proposed for the posture and position representation of CR during movement within bronchus. A contact motion analysis strategy is proposed to calculate real-time attitude of CR in contact with bronchus. In addition, an oscillation suppression approach to address CR's unsmooth distal end trajectory is proposed. Simulated experiments show that the CR autonomously completes intervention tasks with a smooth and stable trajectory, reducing distal end oscillation by over 45%. It achieves a target endpoint within the fourth level bronchus (approximately 5 mm diameter) with over 90% probability.

Cable-driven continuum robots in bronchoscopic procedures hold immense potential to revolutionize the diagnosis and treatment of lung cancer. However, robotic bronchoscopes in current studies are typically large in size and inflexible. Therefore, this article introduces a novel cable-driven continuum robot bronchoscopy system that achieves modular design between the actuation and operation ends. A continuum structure with a dual-segment notched flexible skeleton, featuring a wall thickness of 0.45 mm, has been designed to perform bending movements exceeding 190°. This enhances flexibility and increases the spatial capacity of the working channels. A kinematic model was developed, integrating the actuation force and the mechanical characteristics of the driving cables for error compensation, estimating the correlation between the displacement of the driving cables and the position of the continuum robot’s end-effector. The verification showed that the root mean square error (RMSE) of the end-effector position is 2.57 mm, which accounts for 4.8% of the continuum’s length. A prototype of the robotic bronchoscopy system was created, and its performance and potential applications in bronchoscopic intervention surgeries were validated through vivo pig intervention experiments.