论文标题
通过运动计划，用于任意路由的多键式机器人的交互式速率监督控制
Interactive-Rate Supervisory Control for Arbitrarily-Routed Multi-Tendon Robots via Motion Planning
论文作者
论文摘要
肌腱驱动的机器人，在张力弯曲下的一个或多个肌腱弯曲并操纵柔性骨架，可以改善涉及人体难以到达地区的微创手术。在受约束的解剖环境中安全的计划动作需要在形状估计和碰撞检查方面的准确性和效率。使用任意穿刺肌腱的肌腱机器人可以达到复杂而有趣的形状，从而使其能够前往难以到达的解剖区域。任意路由肌腱驱动的机器人具有不直觉的非线性运动学。因此，我们设想临床医生利用辅助交互式运动计划者在微创手术程序中自动为临床医生指定目的地生成无冲突的轨迹。标准运动规划技术无法通过当前昂贵的肌腱机器人运动模型实现交互式速率运动计划。在这项工作中，我们为任意路由的肌腱驱动的机器人提供了3阶段运动规划系统，具有预先计算阶段，负载阶段和监督控制阶段。我们的系统通过开发快速运动模型（比当前模型快1000倍），快速体素碰撞方法（比标准方法快的27.6倍）实现交互速率，并利用了整个机器人工作区的预先计算的路线图，并具有预先素的顶点和边缘。在模拟实验中，我们表明我们的运动规划方法可以达到高尖端位置的精度，并在分段的倒塌肺胸膜空间解剖环境中平均以14.8 Hz的形式生成计划。我们的结果表明，我们的方法比具有标准FK和碰撞检测方法的流行现成运动计划算法快17,700倍。我们的开源代码可在线提供。
Tendon-driven robots, where one or more tendons under tension bend and manipulate a flexible backbone, can improve minimally invasive surgeries involving difficult-to-reach regions in the human body. Planning motions safely within constrained anatomical environments requires accuracy and efficiency in shape estimation and collision checking. Tendon robots that employ arbitrarily-routed tendons can achieve complex and interesting shapes, enabling them to travel to difficult-to-reach anatomical regions. Arbitrarily-routed tendon-driven robots have unintuitive nonlinear kinematics. Therefore, we envision clinicians leveraging an assistive interactive-rate motion planner to automatically generate collision-free trajectories to clinician-specified destinations during minimally-invasive surgical procedures. Standard motion-planning techniques cannot achieve interactive-rate motion planning with the current expensive tendon robot kinematic models. In this work, we present a 3-phase motion-planning system for arbitrarily-routed tendon-driven robots with a Precompute phase, a Load phase, and a Supervisory Control phase. Our system achieves an interactive rate by developing a fast kinematic model (over 1,000 times faster than current models), a fast voxel collision method (27.6 times faster than standard methods), and leveraging a precomputed roadmap of the entire robot workspace with pre-voxelized vertices and edges. In simulated experiments, we show that our motion-planning method achieves high tip-position accuracy and generates plans at 14.8 Hz on average in a segmented collapsed lung pleural space anatomical environment. Our results show that our method is 17,700 times faster than popular off-the-shelf motion planning algorithms with standard FK and collision detection approaches. Our open-source code is available online.
