Autonomous Laparoscope Control through Unified Mechanics-Based Representation of Multimodal Intraoperative Information

A team led by Xiaojian Li has developed a novel control method for laparoscope-holding robots that addresses the challenge of unifying disparate intraoperative signals. The system maps position data, force/torque sensor readings, and laparoscopic images into a single equivalent-wrench representation in operational space. Using a task-priority scheme, it injects these wrenches into the task space and null space, synthesizing control commands that enforce the remote center of motion (RCM) constraint, enable compliant dragging, and achieve autonomous instrument tracking—all within one consistent framework.

Experiments on a surgical phantom and in vivo porcine trials validated the approach. The robot maintained RCM constraints while reducing sustained trocar-site loading, allowed surgeons to drag the laparoscope compliantly, and automatically tracked instruments in the field of view. This unified mechanics-based representation eliminates the need for hand-tuned multimodal fusion, offering a scalable path toward autonomous surgical assistance. The work, submitted to arXiv (2605.04408), could significantly reduce the burden on human camera operators and improve consistency in minimally invasive procedures.