Sparsity-Infused Position and Orientation Control for Redundant Manipulators

With the expansion of applications for robots, merely considering position control is no longer sufficient to meet practical requirements. Hence, it becomes crucial to develop a control method that synchronizes position and orientation for redundant manipulators. Over time, motion control schemes based on the quadratic programming (QP) have inevitably led to excessive joint movements. In this article, position and orientation control is modeled as a sparse optimization problem from a sparsity perspective. Meanwhile, a collective fuzzy gradient descent (CFGD) solver is designed to address the challenge of sparse position and orientation control for redundant manipulators. Theoretical analyses, simulations, and experiments demonstrate the effectiveness and superiority of the proposed method. The method is expected to provide a precise and efficient control strategy for redundant manipulators in complex tasks by reducing unnecessary joint movements and enhancing the overall performance.