In highly constrained settings, e.g., a tentacle-like 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 (1) a serial manipulator and (2) a concentric tube robot, a tentacle-like medical robot that can bend around anatomical obstacles to safely reach clinically-relevant goal regions.

中文翻译：

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使用运动规划的机器人渐近最优运动学设计。


在高度受限的环境中，例如，类似触手的医疗机器人在体内狭窄的腔室中进行微创手术，对于具有通用运动学设计的机器人来说，在避开障碍物的同时到达所有期望的目标可能很困难或不可能。我们引入了一种设计优化方法来计算运动学设计参数，使单个机器人能够到达尽可能多的理想目标区域，同时避开环境中的障碍物。我们的方法适当地将配置空间中基于采样的运动规划集成到设计空间中的随机优化中，这样，随着时间的推移，我们对设计实现目标的能力的评估的准确性会增加，并且我们选择的设计会接近全局最优。我们证明了我们的方法的渐近最优性，并展示了（1）串行机械手和（2）同心管机器人的模拟性能，同心管机器人是一种类似触手的医疗机器人，可以绕过解剖障碍物弯曲以安全地到达临床相关的目标区域。