Closed-Loop Position Control for Growing Robots Via Online Jacobian Corrections

Closed-Loop Position Control for Growing Robots Via Online Jacobian Corrections
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通过在线雅可比校正对种植机器人进行闭环位置控制

DOI:
10.1109/lra.2021.3095625
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发表时间:
2021
影响因子:
5.2
通讯作者:
Morimoto, Tania K.
Morimoto, Tania K.
中科院分区:
计算机科学2区
文献类型:
--
作者:
Watson, Connor;Obregon, Rosario;Morimoto, Tania K.

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不断发展的连续体机器人由于其固有的顺应性和顺应高度弯曲路径的能力,可以在密闭空间中提供更好的安全性和导航。这些和其他连续体机器人的导航通常涉及与周围环境的频繁交互,这些环境通常是未知的并且可以影响机器人的运动学。我们在这里提出了一种方法,用于控制连续体机器人的基础上利用实时位置和方向测量。该姿态信息用于通过校正旋转和幅度调整以在线方式更新机器人的速度运动学的局部模型。我们结合联合收割机所提出的控制方法与一种方法,用于本地化的尖端的不断增长的机器人和评估的性能闭环位置控制上的一个点达到的任务,在无约束和约束的环境。闭环系统实现了3.221.31毫米的平均总误差在无约束的情况下和4.561.56毫米在约束的情况下,验证我们提出的方法。这项工作也代表了第一种方法的自主位置控制的增长机器人,不需要地图的环境。
Growing continuum robots offer improved safety and navigation in confined spaces, due to their inherent compliance and ability to conform to highly curved paths. Navigation of these, and other continuum robots, often involves frequent interactions with the surrounding environment, which are typically unknown and can affect the kinematics of the robot. We propose here an approach for controlling continuum robots based on leveraging real-time position and orientation measurements. This pose information is used to update a local model of the robot's velocity kinematics in an online manner via corrective rotations and magnitude adjustments. We combine the proposed control approach with a method for localizing the tip of a growing robot and evaluate the performance of closed-loop position control on a point-reaching task in unconstrained and constrained environments. The closed-loop system achieves an average total error of 3.221.31 mm in the unconstrained case and 4.561.56 mm in the constrained case, validating our proposed approach. This work also represents the first method for autonomous position control of growing robots that does not require a map of its environment.
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发表时间: 2020-05
期刊: 2020 IEEE International Conference on Robotics and Automation (ICRA)
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基于观察者的变刚度充气机器人控制 *
DOI: --
发表时间: 2020
期刊: IEEE/RJS International Conference on Intelligent RObots and Systems
影响因子: --
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