Automatic Optimal Design of a Visual-based Stiffness Sensor and real-time Colour-coded Stiffness Map for Minimally Invasive Procedures.

这项研究的实施涉及创建两个系统，一个放置在由机器人机械手和基于视觉的传感机构组成的远程站点，另一个放置在诊室，操作员可以在可视化探索的解剖区域的实时僵硬信息的同时控制和重新规划机器人的运动。已经购买了一个轻型六自由度机械手，UFactory Lite6协作机器人，并与Robot操作系统(ROS)接口，ROS是一个强大的框架，允许实现机器人的算法模拟和与真实硬件的轻松接口。半自主导航算法，操作者可以远程控制机器人的运动并重新规划机器人的路径。虽然操作者可以重新规划机器人的运动并对其进行远程控制，但在接触过程中，操作员将利用视觉刚度信息来控制力并集成安全未来。控制和规划算法已经在仿真中进行了初步测试，并与真实机器人对接。为了找到传感机构的最优设计，实现了一种新颖的三维机器学习算法。该方法仍处于开发阶段。

The implementation of this research is related to the creation of two systems, one placed in the remote site composed by a robot manipulator and a visual based sensing mechanism, and another placed on the clinical room in which an operator can control and replan the motion of the robot while visualizing real-time stiffness information of the explored anatomical area.A light weight six degree of freedom manipulator, the UFactory Lite6 collaborative robot, has been purchased and interfaced with the Robot Operating System (ROS), a powerful framework which allow implementation of algorithms-simulations for robotics and easy interface with real hardware.A semi-autonomous navigation algorithm, in which the operator can control the motion of the robot remotely and replan the its path, has been implemented using Moveit, a motion planning navigation framework, in ROS.Although the operator can replan the motion of the robot and control it remotely, virtual fixture will be implemented using the visual-stiffness information to control the force and integrate safety futures during the contact.The control and planning algorithm have been initially tested in simulations and are now interfaced with the real robot.A novel 3D machine learning algorithm, that shatters the constraints of conventional methodologies, is implemented to find the optimal design of the sensing mechanism. The method is still in a development phase.