In most research works, the conditioning index is used to analyze the kinematic performance of a type of parallel manipulators whose traveling plate has only translation in the workspace, and each limb is made up of three joints and two moving links. However, it is difficult to determine the reasonable limit value of the conditioning index as the design criterion. Therefore, this paper defines two types of transmission angles to evaluate the kinematic performance of parallel manipulators, with which the direct and indirect singularities can be identified in a straightforward manner. Taking both the Diamond and Delta parallel manipulators as examples, the relationships are investigated between the angles and the condition number of the Jacobian, which is a vital performance index for parallel manipulators throughout the workspace. Then, simulation studies are conducted to prove the effectiveness of two kinds of transmission angles for the Diamond and Delta robots. It indicates that the defined angles are well characterized compared with the condition number of the Jacobian matrix in evaluating the kinematic performance of the parallel manipulators and determining the workspace.

Due to their flexibility and operational efficiency, mobile manipulators (MMR) are increasingly applied to the drilling of large-scale thin-walled components. However, the overall structural stiffness of the system varies significantly under different configurations, combined with traditional position-based control strategies, leads to unstable drilling forces and poor contact force regulation, which degrade hole roundness and machining quality. Moreover, wheel-ground slippage during operation further compromises system stability. To address these challenges, this study proposes an adaptive impedance control strategy integrated with stiffness index optimization. A stiffness evaluation index is formulated to describe stiffness variations during posture transitions, and slippage of the mobile platform is analyzed. Both are incorporated as constraints in the optimal drilling posture planning. Furthermore, an impedance interaction model between the MMR and its environment is established to analyze and improve the accuracy of contact force control. Based on the optimized posture planning method and the impedance model, a stiffness-parameter-adaptive impedance controller is designed to significantly enhance the force stability and control accuracy of the end-effector during drilling. This enables precise force-controlled drilling under stiffness-optimized configurations. The effectiveness of the proposed strategy is validated through simulations and physical drilling experiments, demonstrating notable improvements in contact force regulation and hole roundness. The proposed method effectively improves arm stiffness and force control accuracy in drilling operations, thereby enhancing machining performance and system stability. This strategy offers a robust solution to address the inherent low stiffness and control limitations of MMRs, contributing to the advancement of precision manufacturing for large-scale thin-walled components.

Voxel-based LiDAR–inertial odometry (LIO) is accurate and efficient but can suffer from geometric inconsistencies when single-Gaussian voxel models indiscriminately merge observations from conflicting viewpoints. To address this limitation, we propose Azimuth-LIO, a robust voxel-based LIO framework that leverages azimuth-aware voxelization and probabilistic fusion. Instead of using a single distribution per voxel, we discretize each voxel into azimuth-sectorized substructures, each modeled by an anisotropic 3D Gaussian to preserve viewpoint-specific spatial features and uncertainties. We further introduce a direction-weighted distribution-to-distribution registration metric to adaptively quantify the contributions of different azimuth sectors, followed by a Bayesian fusion framework that exploits these confidence weights to ensure azimuth-consistent map updates. The performance and efficiency of the proposed method are evaluated on public benchmarks including the M2DGR, MCD, and SubT-MRS datasets, demonstrating superior accuracy and robustness compared to existing voxel-based algorithms.