微机器人血管内实时避障导航，路径更短更快。
Microrobot Vascular Parkour: Analytic Geometry-based Path Planning with Real-time Dynamic Obstacle Avoidance
- 用解析几何规划全局路径，结合规则与强化学习局部避障。
- 平均每帧40毫秒规划时间，支持25帧/秒实时控制。
- 3D环境下仍保持高速，适合血管靶向药物递送场景。
血管内的自主微机器人可实现微创治疗，但密集且移动的障碍物使其导航困难。本文提出一种实时路径规划框架，将解析几何全局规划器（AGP）与两种反应式局部逃逸控制器（基于规则和强化学习）相结合，以应对突发移动障碍。系统利用实时成像估计微机器人、障碍物和目标的位置，并计算无碰撞运动轨迹。仿真结果显示，AGP在路径长度和规划速度上均优于加权A*（WA*）、粒子群优化（PSO）和快速探索随机树（RRT），同时保持可行性与确定性。我们将AGP从2D拓展至3D，未损失速度。在仿真与实验中，全局规划器与局部控制器协同工作，能可靠避开移动障碍并抵达目标。平均规划时间为每帧40毫秒，兼容25帧/秒图像采集与实时闭环控制。该成果推进了微机器人在血管环境中的自主导航与靶向药物递送能力。
原文摘要 · Abstract (English)
Autonomous microrobots in blood vessels could enable minimally invasive therapies, but navigation is challenged by dense, moving obstacles. We propose a real-time path planning framework that couples an analytic geometry global planner (AGP) with two reactive local escape controllers, one based on rules and one based on reinforcement learning, to handle sudden moving obstacles. Using real-time imaging, the system estimates the positions of the microrobot, obstacles, and targets and computes collision-free motions. In simulation, AGP yields shorter paths and faster planning than weighted A* (WA*), particle swarm optimization (PSO), and rapidly exploring random trees (RRT), while maintaining feasibility and determinism. We extend AGP from 2D to 3D without loss of speed. In both simulations and experiments, the combined global planner and local controllers reliably avoid moving obstacles and reach targets. The average planning time is 40 ms per frame, compatible with 25 fps image acquisition and real-time closed-loop control. These results advance autonomous microrobot navigation and targeted drug delivery in vascular environments.
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