Anderson Patrick L, Mahoney Arthur W, Webster Robert J
Department of Mechanical Engineering at Vanderbilt University, Nashville, TN 37235, USA.
IEEE Robot Autom Lett. 2017 Jul;2(3):1617-1624. doi: 10.1109/LRA.2017.2678606. Epub 2017 Mar 6.
This paper examines shape sensing for a new class of surgical robot that consists of parallel flexible structures that can be reconfigured inside the human body. Known as CRISP robots, these devices provide access to the human body through needle-sized entry points, yet can be configured into truss-like structures capable of dexterous movement and large force application. They can also be reconfigured as needed during a surgical procedure. Since CRISP robots are elastic, they will deform when subjected to external forces or other perturbations. In this paper, we explore how to combine sensor information with mechanics-based models for CRISP robots to estimate their shapes under applied loads. The end result is a shape sensing framework for CRISP robots that will enable future research on control under applied loads, autonomous motion, force sensing, and other robot behaviors.
本文研究了一种新型手术机器人的形状感知问题，该机器人由可在人体内重新配置的平行柔性结构组成。这些设备被称为CRISP机器人，它们通过针大小的入口点进入人体，但可以配置成能够进行灵巧运动和施加大力的桁架状结构。它们还可以在手术过程中根据需要重新配置。由于CRISP机器人具有弹性，它们在受到外力或其他干扰时会变形。在本文中，我们探索如何将传感器信息与基于力学的CRISP机器人模型相结合，以估计它们在施加负载下的形状。最终结果是一个用于CRISP机器人的形状感知框架，这将为未来关于施加负载下的控制、自主运动、力感知和其他机器人行为的研究提供支持。
