Fuzzy-RRT for Obstacle Avoidance in a 2-DOF Semi-Autonomous Surgical Robotic Arm
Abstract
AI-driven semi-autonomous robotic surgery is essential for addressing the medical challenges of long-duration interplanetary missions, where limited crew sizes and communication delays restrict traditional surgical approaches. Current robotic surgery systems require full surgeon control, demanding extensive expertise and limiting feasibility in space. We propose a novel adaptation of the Fuzzy Rapidly-exploring Random Tree algorithm for obstacle avoidance and collaborative control in a two-degree-of-freedom robotic arm modeled on the Miniaturized Robotic-Assisted surgical system. It was found that the Fuzzy Rapidly-exploring Random Tree algorithm resulted in an 743 percent improvement to path search time and 43 percent improvement to path cost.
Cite
@article{arxiv.2504.17979,
title = {Fuzzy-RRT for Obstacle Avoidance in a 2-DOF Semi-Autonomous Surgical Robotic Arm},
author = {Kaaustaaub Shankar and Wilhelm Louw and Bharadwaj Dogga and Nick Ernest and Tim Arnett and Kelly Cohen},
journal= {arXiv preprint arXiv:2504.17979},
year = {2025}
}
Comments
9 pages, 5 figures. Submitted to NAFIPS 2025 Conference (North American Fuzzy Information Processing Society). Includes results on Fuzzy-RRT performance in surgical robotics path planning