Posture control of tensegrity manipulator based on kinematic model using kernel ridge regression
Posture control of tensegrity manipulator based on kinematic model using kernel ridge regression
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DOI：
10.1007/s10015-022-00789-0
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发表时间：
2022-09
影响因子：
0.9
通讯作者：
Yuhei Yoshimitsu;Kenta Tsukamoto;Shuhei Ikemoto
中科院分区：
文献类型：
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作者：
Yuhei Yoshimitsu;Kenta Tsukamoto;Shuhei Ikemoto
生物体有许多多余的软肌肉。在我们的实验室里，我们既追求机器人生物体的柔软性，又追求机器人的冗余性。提出了张拉整体结构在增强机器人柔软性和冗余性方面的适用性。张拉整体结构是由压杆和拉索组成的无压杆连接的结构。这种结构是柔软的，包含许多致动器。在这里，我们开发了一种由20个气缸和20个弹簧驱动的张拉整体机械手，以提高柔软性和冗余度。机器人使用气动气缸移动其电缆不断变化的张力。为了控制该机器人，必须对执行器输入和机器人姿态之间的复杂关系进行建模。在本研究中，正向运动学采用机器学习的方法进行建模。由推导出的正运动学模型推导出雅可比矩阵，对运动学逆解进行了数值求解。这项研究报告了这个简单的运动学模型，并讨论了改进它的方法。
Biological bodies have numerous redundant soft muscles. In our laboratory, we pursue both the softness and redundancy of biological bodies in robots. We propose the suitability of the “tensegrity” structure for enhancing softness and redundancy in robots. Tensegrity is a structure composed of struts and cables without strut connections. This structure is soft and contains numerous actuators. Herein, we develop a tensegrity manipulator driven by 20 pneumatic cylinders with 20 springs to enhance the softness and redundancy. The robot moves the changing tensile forces of its cables using pneumatic cylinders. The complex relationship between the actuator inputs and the robot’s postures to control this robot must be modeled. In this study, the forward kinematics is modeled by machine learning. The inverse kinematics is solved numerically by deriving the Jacobi matrix from the derived forward kinematic model. This study reports on this simple kinematic model and discusses ways to improve it.