New Benchmark for RCM-Constrained Visual Servoing in Laparoscopic Robots

In robot-assisted laparoscopic surgery, accurately enforcing the remote center of motion (RCM) constraint is critical for safe automatic field-of-view adjustment. Until now, systematic comparison of different RCM formulations and image-based visual servoing (IBVS) frameworks was hindered by the lack of a unified, reproducible benchmark. Researchers Jing Zhang and Mengtang Li have addressed this gap with a new open-source simulation framework that integrates three representative RCM modeling approaches and six IBVS-based control architectures within a single velocity-level formulation, enabling controlled and consistent evaluation.

Through structured case studies, the framework identifies key structural sensitivities arising from modeling-controller interactions. These include the impact of tangent-plane definition, constraint dimensionality, open- versus closed-loop enforcement, and robustness near kinematic singularities. All resources, including code and supplementary video demonstrations, are freely released. This work provides a reproducible foundation for future research in RCM-constrained visual servoing, potentially accelerating development of safer autonomous surgical tools.