Background: Accurate and stable instrument positioning is critical in dental implant procedures, particularly in anatomically constrained regions. Conventional navigation systems assume a static patient head, limiting adaptability in dynamic surgical conditions. This study proposes and validates a real-time motion compensation framework that integrates optical motion tracking with a collaborative robot to maintain tool alignment despite patient head movement. Methods: A six-camera OptiTrack Prime 13 system tracked rigid markers affixed to a 3D-printed human head model. Real-time head pose data were streamed to a Kuka LBR iiwa robot, which guided the implant handpiece to maintain alignment with a predefined target. Motion compensation was achieved through inverse trajectory computation and second-order Butterworth filtering to approximate realistic robotic response. Controlled experiments were performed using the MAiRA Pro M robot to impose precise motion patterns, including pure rotations (±30° at 10–40°/s), pure translations (±50 mm at 5–30 mm/s), and combined sinusoidal motions. Each motion profile was repeated ten times to evaluate intra-trial repeatability and dynamic response. Results: The system achieved consistent pose tracking errors below 0.2 mm, tool center point (TCP) deviations under 1.5 mm across all motion domains, and an average latency of ~25 ms. Overshoot remained minimal, with effective damping during motion reversal phases. The robot demonstrated stable and repeatable compensation behavior across all experimental conditions. Conclusions: The proposed framework provides reliable real-time motion compensation for dental implant procedures, maintaining high positional accuracy and stability in the presence of head movement. These results support its potential for enhancing surgical safety and precision in dynamic clinical environments.

(1) Background: Most robotic MIS platforms lack native haptic feedback, leaving surgeons to infer tissue loads from vision alone—an especially risky limitation in esophageal procedures. (2) Methods: We develop a sensorless, image-only force-estimation pipeline that maps endoscopic video to tool–tissue forces using a lightweight EfficientNetV2B0 CNN. The model is trained on 9691 labeled frames from in vitro esophageal experiments and validated against an FT300 load cell. For intraoperative feasibility, the system is deployed as a plug-in on PARA-SILSROB, consuming the existing laparoscope feed and driving a commercial haptic device. The runtime processes every 10th frame of a 60 FPS stream (≈6 Hz updates) with ~15–20 ms per-prediction latency. (3) Results: On held-out tests, the model achieves MAE = 0.017 N and MSE = 0.0004 N², outperforming a recurrent CNN baseline while maintaining real-time performance on commodity hardware. Integrated evaluations confirm stable operation at the deployed update rate and low latency compatible with closed-loop haptics. (4) Conclusions: By avoiding distal force sensors and preserving sterile workflow, the approach is readily translatable and retrofit-friendly for current robotic platforms. The results support the practical feasibility of real-time, sensorless force feedback for robotic esophagectomy and related MIS tasks, with potential to reduce tissue trauma and enhance operative safety.

The paper focuses on the development of a robotic solution for cancer treatment. Liver cancer and especially hepatocellular carcinoma is one of the deadliest causes of death worldwide. Even more, liver cancer leads to more than half of the metastatic pancreatic cancers. Its treatment depends on the tumor staging and other comorbidities of the patient, and if the health condition is poor, minimally invasive procedures are preferred. The ProHep-LCT parallel robotic system has been developed for the minimally invasive treatment of cancer liver using various approaches: brachytherapy, radiofrequency ablation or intra-tumoral drug release, all of which requiring an accurate needle placement. ProHep LCT has consists of two robots: one for tumor visualization using the intraoperative ultrasound technique and the other to deliver the targeted treatment. A research method regarding the onsite placement accuracy of the two modules of the ProHep-LCT using a commercial tracking system is proposed in the paper, to assess its usability for the medical task.

This paper presents the development of the hardware and software architecture of a sixdegrees of freedom (DOF) parallel robot (PARA-SILSROB) by illustrating all the stages undertaken to achieve the experimental model of the robot. Based on the experimental model, the control architecture is also presented, which is primarily based on a master–slave control system through which the surgeon controls the robot using the master console composed of commercial peripheral components (two 3D Space Mouse devices, computer, and keyboard) integrated with the solution developed in this study and presented in this paper. The robot was developed also according to the surgical protocol and surgeon’s requirements, and for the functionality testing of the mechanical structure, two experimental stands were used. The first stand presented several surgical steps, such as manipulation, resection, and suture of experimental tissues (simulating real-life robot-assisted surgical maneuvers) using commercial instruments. The second stand presented a simulation of an esophagectomy for esophageal cancer and digestive reconstruction through a right intercostal approach. For this testing phase, the organs were created using 3D reconstruction, and their simplified models were 3D printed using PolyJet technology. Furthermore, the input trajectory generated using the master console was compared with the robot actuator’s movements and the obtained results were used for validation of the proposed robot control system.

The rapid advancement of robot-assisted minimally invasive surgery (RAMIS) has created an increasing demand for integrated solutions that combine advanced robotic actuation, sensing, and intelligent control within unified training and operational frameworks. This paper presents a Digital Twin–based virtual reality (VR) interaction and control system developed for an innovative parallel surgical robot, designed to support both surgical training and real-time robot interaction. The proposed framework extends a conventional VR simulator into a bidirectional Digital Twin architecture, enabling real-time synchronization between a virtual environment and the physical robotic system. The system integrates the ATHENA parallel robot, characterized by a 4-degree-of-freedom architecture with a Remote Center of Motion (RCM) constraint, together with a flexible laparoscopic instrument providing enhanced dexterity. Interaction is achieved using VR controllers, allowing intuitive manipulation of the robotic system within an immersive environment. To enhance operational performance, an artificial intelligence module based on neural networks is integrated as an assistive component, providing real-time trajectory refinement and motion guidance. The trained model is deployed using an ONNX-compatible runtime, ensuring efficient inference and seamless integration within the control architecture. The proposed system is validated through experimental evaluation of user interaction and task execution performance, as well as through external motion assessment using an OptiTrack optical tracking system. The results demonstrate improvements in motion stability, execution efficiency, and user interaction quality, while maintaining a high level of control intuitiveness. The findings highlight the potential of Digital Twin–based VR systems as a unifying platform for surgical training, interaction, and intelligent assistance in next-generation medical robotic systems.