串行软-刚混合机器人的递归模型无关逆动力学
Recursive Model-agnostic Inverse Dynamics of Serial Soft-Rigid Robots
AI总结：
本文针对串行软-刚混合机器人提出一种模型无关的递归逆动力学算法，仅需几何描述即可获得线性复杂度的精确运动方程，并支持异构降阶模型，解决了现有方法无法处理混合软刚系统的问题。
摘要：
机器人学正从刚性、铰接式系统转向更复杂且异构的机械结构。例如，软体机器人具有能够产生大变形的连续可变形元件。针对这一类系统发展起来的控制技术的繁荣，正推动着对高效评估其逆动力学（ID）程序的需求，而由于这些系统复杂且混合的特性，这一任务具有挑战性。迄今为止，没有单一的逆动力学算法能够描述通用（组合的）软体机器人模型的行为。我们针对可能软结构且可能需要异构建模技术的通用串行类互连来应对这一挑战。我们提出的算法要求输入从构型空间到变形空间映射的纯几何描述（类似正运动学）。仅凭这一信息，完整的运动方程就可以被赋予精确的递归结构，该结构本质上独立于（或“无关”于）底层的降阶运动学建模技术。我们通过利用Kane方法来处理运动方程，并展示其递归结构，从而实现这一目标。所得的逆动力学算法在所提出的设定下具有最优计算复杂度，即与不同模块的数量成线性关系。此外，还引入了该算法的一种变体，可以在不增加计算成本的情况下评估广义质量矩阵。我们展示了该方法适用于涉及刚性和软元件混合的机器人模型，这些模型可能通过异构降阶模型（ROMs）描述，例如体积有限元法（Volumetric FEM）、基于Cosserat应变的模型以及保体积变形基元。这些系统均无法使用现有的逆动力学技术处理。
英文摘要：
Robotics is shifting from rigid, articulated systems to more sophisticated and heterogeneous mechanical structures. Soft robots, for example, have continuously deformable elements capable of large deformations. The flourishing of control techniques developed for this class of systems is fueling the need of efficient procedures for evaluating their inverse dynamics (ID), which is challenging due to the complex and mixed nature of these systems. As of today, no single ID algorithm can describe the behavior of generic (combinations of) models of soft robots. We address this challenge for generic series-like interconnections of possibly soft structures that may require heterogeneous modeling techniques. Our proposed algorithm requires as input a purely geometric description (forward-kinematics-like) of the mapping from configuration space to deformation space. With this information only, the complete equations of motion can be given an exact recursive structure which is essentially independent from (or `agnostic' to) the underlying reduced-order kinematic modeling techniques. We achieve this goal by exploiting Kane's method to manipulate the equations of motion, showing then their recursive structure. The resulting ID algorithms have optimal computational complexity within the proposed setting, i.e., linear in the number of distinct modules. Further, a variation of the algorithm is introduced that can evaluate the generalized mass matrix without increasing computation costs. We showcase the applicability of this method to robot models involving a mixture of rigid and soft elements, described via possibly heterogeneous reduced order models (ROMs), such as Volumetric FEM, Cosserat strain-based, and volume-preserving deformation primitives. None of these systems can be handled using existing ID techniques.
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