A Unified Benchmark for RCM-Constrained Visual Servoing: Modeling–Controller Interaction and Robustness Analysis in Laparoscopic Robots

Jul 16, 2026

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In robot-assisted laparoscopic minimally invasive surgery, enforcing the remote center of motion (RCM) constraint at the trocar is essential for safe, stable automatic field-of-view (FoV) adjustment. The work addresses the lack of a unified, reproducible benchmark for comparing control-based RCM strategies and image-based visual servoing (IBVS) frameworks. An open-source simulation framework integrates three RCM modeling approaches and six IBVS control architectures in a consistent velocity-level formulation, enabling controlled evaluations and robustness analysis near kinematic singularities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Introduction

RCM constraint in visual servoing

Categorization of RCM strategies

Motivation and evaluation gap

Proposed unified simulation benchmark

A Unified Benchmark for RCM-Constrained Visual Servoing: Modeling–Controller Interaction and Robustness Analysis in Laparoscopic Robots Jing Zhang, Mengtang Li, Member, IEEE arXiv :2607 .00030v1 [ cs .RO] 22 Jun 2026 Abstract—In robot-assisted laparoscopic minimally invasive surgery (MIS), accurate enforcement of the remote center of motion (RCM) constraint is critical for safe and stable automatic field-of-view (FoV) adjustment. Although controlbased RCM strategies are widely adopted due to their flexibility and cost-effectiveness, systematic comparison of different RCM formulations and image-based visual servoing (IBVS) frameworks remains challenging due to the lack of a unified and reproducible benchmark. This paper presents an open-source simulation framework integrating three representative RCM modeling approaches and six IBVS-based control architectures within a unified velocity-level formulation, enabling controlled and consistent evaluation. Through structured case studies, the framework reveals key structural sensitivities arising from modeling and controller interactions, including the impact of tangent-plane definition, constraint dimensionality, openversus closed-loop enforcement, and robustness near kinematic singularities. All resources are released and demostrations are provided in the supplementary video, providing a reproducible foundation for RCM-constrained visual servoing research. I. INTRODUCTION Surgical robots have been widely adopted in various medical applications over the past decades due to their high precision and intelligence [1] . In robot-assisted laparoscopic surgery, robots can manipulate the laparoscope to reduce surgeon workload and improve procedural stability [2] . Recent advances in visual servoing [3], [4] and learning-based perception [5], [6] further enable automatic field-of-view (FoV) adjustment during surgery. A fundamental requirement in these systems is the enforcement of the remote center of motion (RCM) constraint at the trocar, which restricts the laparoscope to pivot about a fixed incision point, thereby preventing tissue damage [7] . The RCM methodologies can be broadly categorized into design-based and control-based strategies [7] . Design-based approaches rely on specialized mechanical structures to physically enforce the pivot constraint, as exemplified by commercial systems such as the da Vinci platform. Although mechanically precise, these solutions are often complex and costly [8] . In contrast, control-based RCM strategies enforce the constraint at the kinematic level and can be implemented on general-purpose robotic manipulators without dedicated mechanisms. This flexibility has led to a variety of RCM modeling and control formulations in the literature [9], [10],[11] . All authors are with School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China. Corresponding author: Mengtang Li (e-mail: [limt29@mail.sysu.edu.cn](limt29@mail.sysu.edu.cn)). TABLE I SUMMARY OF AUTOMATIC FOV ADJUSTMENT WORKS WITH CONTROL-BASED RCM METHODS . | RCM type | Work | Robot type | Controller1 | | --- | --- | --- | --- | | Calculation<br>based | Fozilov et al., 2023 [3] | Rigid | QP | | | Zhou et al., 2025 [12] | Rigid | QP | | | Yang et al., 2026 [16] | Rigid | IK | | Variable<br>based | Aghakhani et al., 2013 [11] | Rigid | PI | | | Huang et al., 2025 [14] | Flexible | QP | | | Cao et al., 2025 [15] | Rigid | QP | 1 IK: Inverse kinematics-based; QP: Quadratic programming-based; PI: Pseudoinverse-based. Existing control-based RCM formulations can be divided into two main categories. Calculation-based approaches determine the RCM point through geometric relationships, such as tangent-plane constraints [9] or projection onto the laparoscope axis [10] . These models have been combined with control architectures like quadratic programming (QP) schemes [12], [13] . Variable-based approaches, on the other hand, introduce a vir

FAQs

What problem does the paper address in RCM-constrained visual servoing research?

It addresses the absence of a unified and reproducible benchmark for systematically comparing different RCM formulations and IBVS frameworks under controlled conditions.

What does the proposed framework integrate?

It integrates three representative RCM modeling approaches and six IBVS-based control architectures within a consistent velocity-level formulation.

Which sensitivities does the benchmark study reveal?

The study highlights structural sensitivities from modeling and controller interactions, including tangent-plane definition, constraint dimensionality, open- versus closed-loop enforcement, and robustness near kinematic singularities.

Jul 16, 2026

Upload by Aurora