In this work, we develop a collaborative path-planning method for robotic soft-tissue surgery. The proposed method aims to fill the gaps between current state-of-the-art teleoperation surgery and ideal fully autonomous surgery, which is the long-term goal of surgical robots. Via the proposed method, a human user issues high-level path planning commands that the surgical robot can autonomously execute. Specifically, a human user selects key points on a target tissue via a haptic device and a multi-camera 3D sensing and overlay system. An autonomous path-planning and filtering method then assists the user in completing the path by providing uniformly distributed waypoints on the surface of the tissue that traverse through the key points and avoid undesired regions. We also develop supplementary force feedback and visual cues to further improve the effectiveness of the proposed method by helping the user detect the 3D surface of the tissue more easily. Our results via a human subject study indicate that compared to the unassisted path generation method, the proposed method can reduce the error in spacing between the waypoints by 67.6%, the probability of making path corrections by 33.3%, the completion time by 40.7%, and the perceived workload by 30.5%.
Caputo et al. (2026) studied this question.