Slender medical continuum robots with flexibility and highly redundant degrees of freedom are widely used in various minimally invasive surgery. However, when interacting with anatomical structures, the continuum robot adopts diverse shapes, posing challenges for operation and control. To achieve real-time intraoperative shape sensing and provide online guidance for manipulation, most existing methods rely on optical fibers embedded within the robot, which often require specialized robot designs and come with high costs. Here, we present a novel approach utilizing thin and flexible carbon nanotube piezoresistive fibers as a bandage, helically integrated on the surface of existing slender medical continuum robots for shape sensing. The spatial configuration of the robot is effectively inferred by downsampling the resistance changes along the robot’s body and applying a learning-based method. The results demonstrate that the proposed helically arranged carbon nanotube piezoresistive fibers, combined with a data-driven approach, are capable of reconstructing the robot’s spatial shape. In vitro and ex vivo experiments on animal tissues further highlight its promising potential for enhancing the shape-sensing ability of existing medical continuum robots.

Locating tumors during laparoscopic surgery for early gastric cancers poses an important challenge because they lack involvement with the serosal layer and remain invisible within the peritoneal cavity. To address this issue, various techniques such as preoperative dye injection and magnetic clip detection systems have been introduced to aid in intraoperative tumor localization. However, these existing techniques are often intricate and lack intuition and endurance. In this study, we propose a novel approach utilizing fluorescent soft robots to accurately locate tumors within the stomach. The methodology involved placing a metal clip at the tumor site, followed by administering several soft robots labeled with Cy5. These soft robots were designed to autonomously converge around the metal clip. To validate their efficacy, we conducted animal experiments by implanting clips into the stomachs of rats and subsequently administering capsules containing the soft robots. By detecting the resulting fluorescence, we successfully identified the location of the clips within the stomach. Our findings indicate that these soft robots hold great promise as a viable alternative for localizing gastric lesions during laparoscopic surgery, which has better persistence and intuitiveness than other markup methods. Their implementation could significantly enhance the accuracy and efficiency of tumor identification in a technologically advanced and clinically accessible manner.