用采样MPC规划机器人激光消融路径，精准避开重要组织
Sampling-Based Model Predictive Control for Volumetric Ablation in Robotic Laser Surgery
- 基于采样MPC，通过随机搜索探索可达到的组织状态空间
- 在考虑参数不确定性的前提下，生成不触碰神经血管的消融序列
- 适合需高精度、避让敏感结构的机器人激光手术场景
基于激光的外科消融严重依赖医生操作，精度受限于人类误差。激光与组织的相互作用受多种参数调控，包括激光功率、距离、光斑大小、方向和暴露时间。这种复杂关系适合机器人自动化，使医生可专注于选择消融区域和方式，而低层规划由系统自动完成。本文提出一种基于采样的模型预测控制（MPC）方案，用于规划任意组织体积的消融序列。利用稳态点消融模型模拟单次激光-组织作用，通过随机搜索技术探索可达状态空间，同时保护敏感组织区域。所提出的采样MPC策略在考虑参数不确定性的情况下，生成满足约束条件（如避开关键神经束或血管）的消融序列。
原文摘要 · Abstract (English)
Laser-based surgical ablation relies heavily on surgeon involvement, restricting precision to the limits of human error. The interaction between laser and tissue is governed by various laser parameters that control the laser irradiance on the tissue, including the laser power, distance, spot size, orientation, and exposure time. This complex interaction lends itself to robotic automation, allowing the surgeon to focus on high-level tasks, such as choosing the region and method of ablation, while the lower-level ablation plan can be handled autonomously. This paper describes a sampling-based model predictive control (MPC) scheme to plan ablation sequences for arbitrary tissue volumes. Using a steady-state point ablation model to simulate a single laser-tissue interaction, a random search technique explores the reachable state space while preserving sensitive tissue regions. The sampled MPC strategy provides an ablation sequence that accounts for parameter uncertainty without violating constraints, such as avoiding critical nerve bundles or blood vessels.
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