arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2602.18655cs.ROcs.SYeess.SY
收藏

通过神经算子实现软机器人无限维闭环逆运动学

Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators

Department of Mechanical Engineering, Stanford University(机械工程系,斯坦福大学);Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen–Nürnberg(应用力学研究所,弗赖堡-艾尔兰根-纽伦堡大学);Cognitive Robotics Department, Delft University of Technology(认知机器人系,代尔夫特理工大学)

Carina Veil, Moritz Flaschel, Ellen Kuhl, Cosimo Della Santina

AI总结:

本文提出通过神经算子网络解决软机器人无限维闭环逆运动学问题,结合驱动到形状的映射与形状到任务的映射,实现基于雅可比的CLIK算法。

摘要:

对于完全驱动的刚性机器人,运动学逆问题是一个纯粹的几何问题,可以通过闭环逆运动学(CLIK)方案高效解决,该方案通过计算关节配置来定位机器人本体。然而,对于欠驱动的软机器人,并非所有配置都可以通过控制动作实现,这使得运动学逆问题极具挑战性。CLIK的扩展通过引入从驱动到任务空间的端到端映射,使控制器能够操作,但通常假设底层虚拟配置空间具有有限维度。在本工作中,我们将在无限维域中公式化CLIK,以在解决任务的同时考虑整个软机器人形状。我们通过将驱动到形状的映射与形状到任务的映射相结合,推导出无限维链式法则下的微分端到端运动学,从而获得基于雅可比的CLIK算法。由于这种驱动到形状的映射很少有闭式表达,我们提出使用可微神经算子网络来学习它。我们首先对一个等曲率段进行分析研究,然后将该算法的神经版本应用于一个三纤维软体机器人臂,其底层模型依赖于形态弹性理论和主动纤维理论。

英文摘要:

For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) schemes that compute joint velocities to position the robot body in space. For underactuated soft robots, however, not all configurations are attainable through control action, making kinematic inversion extremely ill-posed. Extensions of CLIK address this by introducing end-to-end mappings from actuation to task space for the controller to operate on, but typically assume finite dimensions of the underlying virtual configuration space. In this work, we formulate CLIK in the infinite-dimensional domain to reason about the entire soft robot shape while solving tasks, thereby skipping intermediate approximation steps. We do this by composing an actuation-to-shape map with a shape-to-task map, deriving the differential end-to-end kinematics via an infinite-dimensional chain rule, and thereby obtaining a Jacobian-based CLIK algorithm. Since complex soft-robot models are rarely available in closed form, and neither the actuation-to-shape mapping nor its Jacobian can typically be obtained analytically, we propose to learn it using differentiable neural operator networks. We first present an analytical study on a constant-curvature segment, and then apply the neural version of the algorithm to a three-fiber soft robotic arm whose underlying model relies on morphoelasticity and active filament theory.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑