Optimizing high-degree-of-freedom robotic manipulators requires searching complex, high-dimensional configuration spaces, a task that is computationally challenging for classical methods. This letter introduces a quantum-native framework that integrates Quantum Machine Learning (QML) with Grover's algorithm to solve kinematic optimization problems efficiently. A parameterized quantum circuit is trained to approximate the forward kinematics model, which then constructs an oracle to identify optimal configurations. Grover's algorithm leverages this oracle to provide a quadratic reduction in search complexity. Demonstrated on simulated 1-DoF, 2-DoF, and dual-arm manipulator tasks, the method achieves significant speedups—up to 93x over classical optimizers like Nelder-Mead—as problem dimensionality increases. This work establishes a foundational, quantum-native framework for robot kinematic optimization, effectively bridging quantum computing and robotics problems.

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.

Assembly of the low-pressure turbine rotor (LPTR) of an aero-engine requires the principal shaft to be inserted along the center of the aero-engine shaft hole precisely. The application of appropriate sensors is imperative for ensuring these aspects. In this paper, a custom automated assembly system (AAS) is developed for the LPTR assembly. First, we describe the overall structure and operation of the AAS. The sensor technology employed in the system is also described in detail, including its composition and principle of operation. Second, the kinematic model of attitude adjustment unit (AAU) is established based on the pose of the aero-engine and the LPTR. The kinematic equation of the AAS is developed based on the position closure approach which is more compact and coordinate free unlike the D-H method. Furthermore, the working principle of the integrated management control system of the working parts is introduced. Finally, the one-time assembly success rate and the assembly time are tested to verify the performance of the AAS. The results showed that the one-time assembly success rate and the assembly time both improvement compared to manual. Therefore, the application of many types of sensors is beneficial for the automation and precision assembly of the AAS.

This study presents a systematic motion analysis and classification of $SO(3)$-type parallel robot variants using an analytical Lie algebra approach. These robots are known for their ability to perform arbitrary rotations around a fixed point, making them suitable for various applications. Despite their architectural diversity, existing research has largely treated them on a case-by-case basis, limiting the exploration of all potential variants and the benefits derived from this diversity. By applying a generalized analytical approach through the reciprocal screw method, we systematically examine the kinematic conditions for limbs that generate $SO(3)$ motion. As a result, we identify 73 distinct non-redundant limb types capable of producing the desired $SO(3)$ motion. Our approach includes an in-depth algebraic motion-constraint analysis, uncovering common characteristics across different variants. This leads us to identify 73 symmetric and 5,256 asymmetric variants, for a total of 5,329, each with unique capabilities. Finally, we selected a computationally optimized, miniaturized robot from this set for use in a humanoid eye system.

Assembly of the low-pressure turbine rotor (LPTR) of an aero-engine requires the principal shaft to be inserted along the center of the aero-engine shaft hole precisely. The application of appropriate sensors is imperative for ensuring these aspects. In this paper, a custom automated assembly system (AAS) is developed for the LPTR assembly. First, we describe the overall structure and operation of the AAS. The sensor technology employed in the system is also described in detail, including its composition and principle of operation. Second, the kinematic model of attitude adjustment unit (AAU) is established based on the pose of the aero-engine and the LPTR. The kinematic equation of the AAS is developed based on the position closure approach which is more compact and coordinate free unlike the D-H method. Furthermore, the working principle of the integrated management control system of the working parts is introduced. Finally, the one-time assembly success rate and the assembly time are tested to verify the performance of the AAS. The results showed that the one-time assembly success rate and the assembly time both improvement compared to manual. Therefore, the application of many types of sensors is beneficial for the automation and precision assembly of the AAS.