Design Optimization of a Parallel Robot for Laparoscopic Pancreatic Surgery Using a Genetic Algorithm

Background: Laparoscopic pancreatic surgery demands high precision and minimal invasiveness, yet conventional robotic systems often face challenges due to complex anatomical environments and uncertainties inherent in surgical procedures. Optimizing key design parameters such as the Remote Center of Motion (RCM) and robotic link lengths is critical for enhancing workspace accessibility and instrument maneuverability. Methods: An integrated optimization framework combining genetic algorithms (GA) with fuzzy logic was developed to determine the optimal RCM position and the ideal lengths of crucial robotic links in a 3-DOF parallel robotic system. The GA explored a large design space based on 6951 tracking points recorded during manual instrument manipulation, while the fuzzy logic system refined fitness evaluations by incorporating expert-defined membership functions and heuristic rules to manage uncertainties and ensure robust performance. Results: Simulation studies demonstrated that the optimized RCM position shifted from an initial [100, 0, 300] to [119.003337, −146.610801, 269.07376], yielding improved workspace coverage and enhanced instrument maneuverability. The GA further determined optimal link lengths of approximately 213.5 mm, 248.5 mm, and 48.6 mm for the primary, tertiary, and minimum secondary links, respectively, which were rounded to practical values of 215 mm, 250 mm, and 50 mm. The optimized design exhibited significant improvements in workspace reachability, precision, and operational stability, as validated by detailed 3D workspace plots and time history diagrams of the instrument tip and joint trajectories. Conclusions: The integrated GA–fuzzy optimization approach effectively enhances the design of a 3-DOF parallel robot for laparoscopic pancreatic surgery by achieving superior kinematic performance. The optimized parameters contribute to improved surgical precision and workspace accessibility, indicating strong potential for clinical application and further experimental validation.