## 4 Discussion
By carefully optimizing the fuzzy rules and membership functions in such a manner to limit the number of linguistic variables used by the fuzzy logic controller, it is possible to reduce computational complexity. Implementing high-performance computational modules like Graphics Processing Units (GPU\({}_{\text{s}}\)) may lead to a considerable increase in the computational speed. Another promising approach involves employing adaptive and incremental fuzzy inference methods, which dynamically adjust their outputs by only recalculating altered inference paths. This incremental updating dramatically decreases computational load and latency without compromising accuracy, making the robotic system highly responsive to dynamic surgical environments. The simulation results further validated the optimized design. Specifically, simulations performed with Siemens NX 2406 highlighted the practical feasibility and potential reliability of the robotic system in a real clinical context. The virtual test from initial to target positions exhibited accurate and stable operation, emphasizing the optimized robot's capability to navigate precise paths required during surgical interventions. The time history diagrams for the instrument tip and active joints further confirmed the system's operational efficiency and stability. Despite the positive outcomes, some considerations remain for future improvements. Firstly, the current optimization framework should be complemented by empirical validation through phantom trials to confirm practical performance and patient outcomes. Secondly, further refinement of fuzzy rules and membership functions based on extensive experimental feedback could enhance the robot's adaptability to a broader range of surgical scenarios. Additionally, the potential integration of machine learning techniques could further refine the fuzzy optimization framework, automating parameter adjustments based on large datasets obtained from real-world operations. The genetic algorithm based on fuzzy logic fitness evaluation has proven itself an effective method for enhancing robotic surgical designs, offering superior flexibility, precision, and operational relevance compared to traditional deterministic methods. This approach presents a promising direction for further advancements in robotic surgical systems, particularly for complex procedures like pancreatic surgery. Diversity in patient anatomy and tissue characteristics is a major challenge in robotic surgery. The variability in the stiffness of tissues, their shapes, and their relative positions adds uncertainty to the actual surgical environment as well as the workspace and the interaction mechanisms between the surgical robotic instruments and the tissues. This problem is manageable with imaging and modeling specific to the patient prior to the surgery, as this enables optimization processes geared to every unique situation. Furthermore, the incorporation of intraoperative feedback mechanisms such as force feedback allow for modifications to the robot's configuration in real time, which in turn accommodates the variability in anatomy. Issues such as mechanical tolerances and the deformation of instruments also impact the accuracy of positioning. While performing the surgery, certain components like flexible shafts and joints have the potential to undergo deformation or backlash, which affect precision and repeatability. To overcome these issues, high-fidelity mechanical modeling with detailed tolerance design is essential. Additionally, closed-loop control approaches that make use of feedback from the robots in real time enhance the ability to compensate for mechanical changes that deviate from the ideal design, which guarantees that the instruments stay on the pre-defined path. Further development of the robotic system presented within this paper focuses on the development of real-time control strategies to mitigate environmental disturbances and integrating machine learning techniques to dynamically adapt the robotic system to the operating field.