Baykal Cenk, Bowen Chris, Alterovitz Ron
Massachusetts Institute of Technology, Cambridge, MA, USA,
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,
Auton Robots. 2019 Feb;43(2):345-357. doi: 10.1007/s10514-018-9766-x. Epub 2018 Jun 29.
In highly constrained settings, e.g., a tentaclelike medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obstacles. We introduce a design optimization method to compute kinematic design parameters that enable a single robot to reach as many desirable goal regions as possible while avoiding obstacles in an environment. Our method appropriately integrates sampling based motion planning in configuration space into stochastic optimization in design space so that, over time, our evaluation of a design's ability to reach goals increases in accuracy and our selected designs approach global optimality. We prove the asymptotic optimality of our method and demonstrate performance in simulation for (i) a serial manipulator and (ii) a concentric tube robot, a tentacle-like medical robot that can bend around anatomical obstacles to safely reach clinically- relevant goal regions.
在高度受限的环境中，例如，一个类似触手的医疗机器人在体内狭窄腔隙中进行微创手术时，具有通用运动学设计的机器人可能难以或无法在避开障碍物的同时到达所有期望目标。我们引入一种设计优化方法来计算运动学设计参数，使单个机器人在避开环境中障碍物的同时能够到达尽可能多的期望目标区域。我们的方法将基于采样的构型空间运动规划适当地集成到设计空间中的随机优化中，以便随着时间的推移，我们对设计到达目标能力的评估在准确性上有所提高，并且我们选择的设计接近全局最优。我们证明了我们方法的渐近最优性，并在模拟中展示了（i）一个串联机械手和（ii）一个同心管机器人（一种类似触手的医疗机器人，可绕过解剖学障碍物以安全到达临床相关目标区域）的性能。
