Interactive-Rate Supervisory Control for Arbitrarily-Routed Multitendon Robots via Motion Planning
Interactive-Rate Supervisory Control for Arbitrarily-Routed Multitendon Robots via Motion Planning
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DOI：
10.1109/access.2022.3194515
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Kuntz, Alan
中科院分区：
文献类型：
--
作者：
Bentley, Michael;Rucker, Caleb;Kuntz, Alan
肌腱驱动的机器人，一条或多条肌腱在张力下弯曲并操纵灵活的脊椎，可以改进涉及人体难以触及的区域的微创手术。在受限的解剖环境中安全地规划运动需要在形状估计和碰撞检查方面的准确性和效率。使用任意路径的肌腱的肌腱机器人可以实现复杂而有趣的形状，使它们能够旅行到难以到达的解剖区域。任意路径的绳索驱动机器人具有非直观的非线性运动学。因此，我们设想临床医生利用辅助交互式运动规划器，在微创手术过程中自动生成到达临床医生指定目的地的无碰撞轨迹。使用目前昂贵的肌腱机器人运动学模型，标准的运动规划技术不能实现交互式速率运动规划。在这项工作中，我们提出了一个包括预计算阶段、加载阶段和监督控制阶段的任意路径腱驱动机器人的三阶段运动规划系统。我们的系统通过开发快速运动学模型(比当前模型快1000倍以上)、快速体素碰撞方法(比标准方法快27.6倍)以及利用预先计算的具有预先体素化顶点和边的整个机器人工作空间的路线图来实现交互速度。在模拟实验中，我们的运动规划方法达到了很高的尖端位置精度，在分段塌陷的肺胸膜空间解剖环境中生成的规划平均频率为14.8赫兹。我们的结果表明，我们的方法比使用标准FK和碰撞检测方法的流行的现有运动规划算法快17,700倍。我们的开源代码可以在网上获得。
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.