DNN-Augmented Kinematically Decoupled Three-DoF Origami Parallel Robot for High-Precision Heave and Tilt Control
Gaokun Shi, Hassen Nigatu, Zhijian Wang, Yongsheng Huang
- Year
- 2025
- Citations
- 2
- Access
- Open access
Abstract
This paper presents a three-degrees-of-freedom origami parallel robot that is free from parasitic motion. This robot is designed to achieve one translational and two rotational motions within its workspace, enabling precise orientation about a fixed point—a capability unattainable for parallel robots with parasitic motion. The elimination of parasitic motion is critical, allowing the use of this device in applications requiring high precision. The robot’s key kinematic features include a parasitic motion-free workspace, large orientational capability, compactness, decoupled motion, simplicity in manufacturing and control, mechanically pivoted rotation of the moving platform, and scalability. These characteristics make the robot particularly well-suited for micromanipulation tasks in both manufacturing and medical applications. In manufacturing, it can enable high-precision operations such as micro-assembly, optical fiber alignment, and semiconductor packaging. In medicine, it can support delicate procedures such as microsurgery and cell injection, where sub-micron accuracy, high stability, and precise motion decoupling are critical requirements. The use of nearly identical limbs simplifies the architecture, facilitating easier design, manufacture, and control. The kinematics of the robot is analyzed using reciprocal screw theory for an analytic constraint-embedded Jacobian. To further enhance operational accuracy and robustness, particularly in the presence of uncertainties or disturbances, a deep neural network (DNN)-based state estimation method is integrated, providing accurate forward kinematic predictions. The construction of the robot utilizes origami-inspired limbs and joints, enhancing miniaturization, manufacturing simplicity, and foldability. Although capable of being scaled up or further miniaturized, its current size is 66 mm × 68 mm × 100 mm. The robot’s moving platform is theoretically and experimentally proven to be free of parasitic motion and possesses a large orientation capability. Its unique features are demonstrated, and its potential for high-precision applications is thoroughly discussed.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002