An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery

2026-03-09

AI Summary

This work addresses the limitations of existing cable-driven surgical robotic platforms, which struggle to maintain state-space consistency and thus hinder the training of reliable autonomous control policies. To overcome this, we present an open-source, robot-agnostic laparoscopic surgery research platform that employs a closed-form analytical velocity solver to achieve deterministic remote center of motion (RCM) control without iterative optimization, thereby strictly enforcing trocar constraints in Cartesian space for industrial manipulators. Built on a full-stack ROS architecture, the platform integrates UR5e and Franka Panda arms, stereo 3D vision, teleoperation, and kinesthetic teaching via a decoupled server-client design. In phantom, ex vivo, and in vivo porcine intestinal grasping tasks, the system achieves sub-millimeter RCM deviation and trajectory smoothness (measured by SPARC and LDLJ) comparable to expert da Vinci performance, marking the first demonstration of high-precision, high-smoothness collaborative control across industrial robotic platforms in real laparoscopic settings.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Autonomous robot-assisted surgery demands reliable, high-precision platforms that strictly adhere to the safety and kinematic constraints of minimally invasive procedures. Existing research platforms, primarily based on the da Vinci Research Kit, suffer from cable-driven mechanical limitations that degrade state-space consistency and hinder the downstream training of reliable autonomous policies. We present an open-source, robot-agnostic Remote Center of Motion (RCM) controller based on a closed-form analytical velocity solver that enforces the trocar constraint deterministically without iterative optimization. The controller operates in Cartesian space, enabling any industrial manipulator to function as a surgical robot. We provide implementations for the UR5e and Franka Emika Panda manipulators, and integrate stereoscopic 3D perception. We integrate the robot control into a full-stack ROS-based surgical robotics platform supporting teleoperation, demonstration recording, and deployment of learned policies via a decoupled server-client architecture. We validate the system on a bowel grasping and retraction task across phantom, ex vivo, and in vivo porcine laparoscopic procedures. RCM deviations remain sub-millimeter across all conditions, and trajectory smoothness metrics (SPARC, LDLJ) are comparable to expert demonstrations from the JIGSAWS benchmark recorded on the da Vinci system. These results demonstrate that the platform provides the precision and robustness required for teleoperation, data collection and autonomous policy deployment in realistic surgical scenarios.

Problem

• autonomous surgery
• robotic platform
• RCM constraint
• state-space consistency
• minimally invasive surgery

Innovation

• Remote Center of Motion (RCM)
• open-source surgical robotics
• analytical velocity solver
• robot-agnostic control
• ROS-based platform