具有运动学不确定性的机器人自适应控制中最具挑战性的问题之一是要求回归形式的雅可比矩阵求逆。这种要求是不可避免的情况下，控制的并联机器人，其动力学方程直接写在任务空间。基于雅可比矩阵的回归形式，设计了一种并联机器人自适应控制器，保证了机器人轨迹的渐近跟踪。其主要思想是将雅可比矩阵的行列式与伴随矩阵分离，并组织新的回归量形式。对2-DOF R \underline{P} R和3-DOF冗余度索驱动机器人的仿真和实验结果表明，所提出的方法具有良好的性能。
英文摘要：
One of the most challenging issues in adaptive control of robot manipulators with kinematic uncertainties is requirement of the inverse of Jacobian matrix in regressor form. This requirement is inevitable in the case of the control of parallel robots, whose dynamic equations are written directly in the task space. In this paper, an adaptive controller is designed for parallel robots based on representation of Jacobian matrix in regressor form, such that asymptotic trajectory tracking is ensured. The main idea is separation of determinant and adjugate of Jacobian matrix and then organize new regressor forms. Simulation and experimental results on a 2--DOF R\underline{P}R and 3--DOF redundant cable driven robot, verify promising performance of the proposed methods.
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）
An analysis of higher-order kinematics formalisms for an innovative surgical parallel robot
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；Gheorghe Asachi Technical University of Iasi（雅西格奥尔基·阿萨基技术大学）；Technical Sciences Academy of Romania（罗马尼亚技术科学院）；École Centrale Nantes（南特中央理工学院）；Nantes Université（南特大学）；CNRS（法国国家科学研究中心）；LS2N（南特数字科学实验室）
Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology（贝克曼高等科学技术研究所）；University of Michigan（密歇根大学）
