用模糊RRT算法提升手术机器臂避障效率，适合太空医疗场景。
Fuzzy-RRT for Obstacle Avoidance in a 2-DOF Semi-Autonomous Surgical Robotic Arm
- 结合模糊逻辑改进RRT算法，实现机器人自主避障与协作控制。
- 路径搜索速度提升743%，路径成本降低43%。
- 适用于空间站等资源受限的远程手术场景。
人工智能驱动的半自主机器人外科手术对解决长期星际任务中的医疗挑战至关重要，因乘员数量有限且通信延迟高，传统手术方式不可行。现有机器人手术系统需完全由外科医生操控，依赖深厚经验，难以在太空中实施。本文提出一种针对两自由度机器人臂（模拟微型机器人辅助手术系统）的模糊快速探索随机树（Fuzzy-RRT）算法，用于障碍物规避与协同控制。实验表明，该算法使路径搜索时间提升743%，路径代价降低43%。
原文摘要 · Abstract (English)
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.
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