Different from traditional redundant manipulators, the redundant manipulators used in the surgical environment require the end effector (EE) to have high pose (position and orientation) accuracy to ensure the smooth progress of the operation. When analyzing the inverse kinematics (IK) of traditional redundant manipulators, gradient-projection method (GPM) and weighted least-norm (WLN) method are commonly used methods to avoid joint position limits. However, for the traditional GPM and WLN method, when joints are close to their limits, they stop moving, which greatly reduces the accuracy of the IK solution. When robotic manipulators enter a singular region, although traditional damped least-squares (DLS) algorithms are used to handle singularities effectively, motion errors of the EE will be introduced. Furthermore, selecting singular region through trial and error may cause some joint velocities exceed their corresponding limits. More importantly, traditional DLS algorithms cannot guide robotic manipulators away from singular regions. Inspired by the merits of GPM, WLN, and DLS methods, an improved weighted gradient projection method (IWGPM) is proposed to solve the IK problem of redundant manipulators used in the surgical environment with avoiding joint position limits and singularities. The weighted matrix of the WLN method and the damping factor of the DLS algorithm have been improved, and a joint limit repulsive potential field function and singular repulsive potential field function belong to the null space are introduced to completely keep joints away from the damping interval and redundant manipulators away from the unsafe region. To verify the validity of the proposed IWGPM, simulations on a 7 degree of freedom (DOF) redundant manipulator used in laparoscopic surgery indicate that the proposed method can not only achieve higher accuracy IK solution but also avoid joint position limits and singularities effectively by comparing them with the results of the traditional GPM and WLN method, respectively. Furthermore, based on the proposed IWGPM, simulation tests in two cases show that joint position limits have a great impact on the orientation accuracy, and singular potential energy function has a great impact on the position accuracy.

Rapid analysis of viscous oil samples is of great interest in food science and the food industry. Herein, neutral desorption sampling in conjunction with dielectric barrier discharge ionization mass spectrometry (ND-DBDI-MS) has been established for the fast and accurate identification of various hogwash oil (HHO) and edible oil samples under ambient conditions. Mass spectra in the negative ion detection mode were recorded in the mass range of m/z 50–500 Da, and characteristic substances responsible for the classification were identified using MS/MS experiments. Particularly, free fatty acids (e.g., oleic acid, linoleic acid, palmitic acid, etc.), typical representatives of oil quality, were successfully measured and used as decisive markers to differentiate HHO from qualified edible oil samples with the help of principal component analysis (PCA). Methodological reproducibility was characterized in terms of statistical method such as cluster analysis (CA). The experimental results show that ND-DBDI-MS is an important tool for the rapid analysis of highly complex viscous samples such as oil samples, with potential applications in food safety analysis.

Robotic manipulators inevitably encounter singular configurations in the process of movement, which seriously affects their performance. Therefore, the identification of singular configurations is extremely important. However, serial manipulators that do not meet the Pieper criterion cannot obtain singular configurations through analytical methods. A joint angle parameterization method, used to obtain singular configurations, is here creatively proposed. First, an analytical method based on the Jacobian determinant and the proposed method were utilized to obtain their respective singular configurations of the Stanford manipulator. The singular configurations obtained through the two methods were consistent, which suggests that the proposed method can obtain singular configurations correctly. Then, the proposed method was applied to a seven-degree-of-freedom (7-DOF) serial manipulator and a planar 5R parallel manipulator. Finally, the correctness of the singular configurations of the 7-DOF serial manipulator was verified through the shape of the end-effector velocity ellipsoid, the value of the determinant, the value of the condition number, and the value of the manipulability measure. The correctness of singular configurations of the planar 5R parallel manipulator was verified through the value of the determinant, the value of the condition number, and the value of the manipulability measure.

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5‐DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

In engineering practice, bearings used as support components are usually installed in pairs in various rotating machines. The mechanical and contact characteristics of bearings play a crucial role in determining the operational accuracy, safety performance and stability of equipment. Numerous studies focused on single bearings have been reported form diffident aspects for enhancing service performance. However, the literature reports on the matched bearings are scarce due to the mutual coupling of rolling elements and the extremely complex stress conditions. Especially the stiffness fluctuation and internal contact status affected by the ball position are difficult to be observed and tested experimentally because of the complex and compact structure, which significantly impacts the safe and reliable operation of rotating machines. To overcome the above shortcomings, this paper investigates the effect of ball position on the stiffness characteristics and contact status of matched angular contact ball bearings based on the coordinate matrix transformation approach and Jones’ statics model. Then, the stiffness results of published literature are used as a benchmark to validate the accuracy of solution method of this paper. Finally, 7206B bearings with different installing forms are used as a numerical example to reveal the effect of ball position on the stiffness characteristics and contact status of matched angular contact ball bearings under different working conditions. The study provides a scientific guidance to investigate the mechanical characteristics and contact mechanism of multi-bearing system.